mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF
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Classify the node 'Where Do SE-trees Perform? (Part I): As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We empirically characterize domains in which SE-trees are particularly advantageous relative to decision trees. Specifically, we show that:' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
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Classify the node ' Finding structure in reinforcement learning. : Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces. This paper presents the SKILLS algorithm. SKILLS discovers skills, which are partially defined action policies that arise in the context of multiple, related tasks. Skills collapse whole action sequences into single operators. They are learned by minimizing the compactness of action policies, using a description length argument on their representation. Empirical results in simple grid navigation tasks illustrate the successful discovery of structure in reinforcement learning.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node ' "Active Learning with Statistical Models," : For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. The authors were also funded by the McDonnell-Pew Foundation, ATR Human Information Processing Laboratories, Siemens Corporate Research, NSF grant CDA-9309300 and by grant N00014-94-1-0777 from the Office of Naval Research. Michael I. Jordan is a NSF Presidential Young Investigator. A version of this paper appears in G. Tesauro, D. Touretzky, and J. Alspector, eds., Advances in Neural Information Processing Systems 7. Morgan Kaufmann, San Francisco, CA (1995).' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
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Classify the node ' Reinforcement Learning for Job-Shop Scheduling, : We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference algorithm T D() is applied to train a neural network to learn a heuristic evaluation function over states. This evaluation function is used by a one-step looka-head search procedure to find good solutions to new scheduling problems. We evaluate this approach on synthetic problems and on problems from a NASA space shuttle payload processing task. The evaluation function is trained on problems involving a small number of jobs and then tested on larger problems. The TD sched-uler performs better than the best known existing algorithm for this task|Zweben's iterative repair method based on simulated annealing. The results suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node 'Adapting Abstract Knowledge: For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a wide range of new situations. In this paper, we address the task of adapting abstract knowledge about planning to fit specific planning situations. First we show that adapting abstract cases requires reconciling incommensurate representations of planning situations. Next, we describe a representation system, a memory organization, and an adaptation process tailored to this requirement. Our approach is implemented in brainstormer, a planner that takes abstract advice.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
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Classify the node ' Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules, Workshop on Constructive Induction and Change of Representation, : This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The presented method learns such descriptions, which we call conditional M-of-N rules, using the hypothesis-driven constructive induction approach. In this approach, the representation space is modified according to patterns discovered in the iteratively generated hypotheses. The need for the M-of-N rules is detected by observing "exclusive-or" or "equivalence" patterns in the hypotheses. These patterns indicate symmetry relations among pairs of attributes. Symmetrical attributes are combined into maximal symmetry classes. For each symmetry class, the method constructs a "counting attribute" that adds a new dimension to the representation space. The search for hypothesis in iteratively modified representation spaces is done by the standard AQ inductive rule learning algorithm. It is shown that the proposed method is capable of solving problems that would be very difficult to tackle by any of the traditional symbolic learning methods.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
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Classify the node ' An evolutionary tabu search algorithm and the NHL scheduling problem, : We present in this paper a new evolutionary procedure for solving general optimization problems that combines efficiently the mechanisms of genetic algorithms and tabu search. In order to explore the solution space properly interaction phases are interspersed with periods of optimization in the algorithm. An adaptation of this search principle to the National Hockey League (NHL) problem is discussed. The hybrid method developed in this paper is well suited for Open Shop Scheduling problems (OSSP). The results obtained appear to be quite satisfactory.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
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Classify the node ' (1991) Global optimization by means of distributed evolution Genetic Algorithms in Engineering and Computer Science Editor J. : Genetic Algorithms (GAs) are powerful heuristic search strategies based upon a simple model of organic evolution. The basic working scheme of GAs as developed by Holland [Hol75] is described within this paper in a formal way, and extensions based upon the second-level learning principle for strategy parameters as introduced in Evolution Strategies (ESs) are proposed. First experimental results concerning this extension of GAs are also reported.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
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Classify the node ' Generalization in reinforcement learning: Successful examples using sparse coarse coding. : On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to generalize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and computational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned o*ine. Boyan and Moore and others have suggested that the problems they encountered could be solved by using actual outcomes ("rollouts"), as in classical Monte Carlo methods, and as in the TD() algorithm when = 1. However, in our experiments this always resulted in substantially poorer performance. We conclude that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general .' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node ' Dynamical selection of learning algorithms. : Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example's class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. [Aha92, Breiman84]) or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the "example space" and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are formed by the observed prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region which is defined by the "cross-validation history." This paper introduces DS, a method for the dynamic selection of a learning algorithm(s). We call it "dynamic" because the learning algorithm(s) used to classify a novel example depends on that example. Preliminary experimentation motivated DW, an extension to DS that dynamically weights the learners predictions according to their regional accuracy. Further experimentation compares DS and DW to a collection of other meta-learning strategies such as cross-validation ([Breiman84]) and various forms of stacking ([Wolpert92]). In this phase of the experiementation, the meta-learners have six constituent learners which are heterogeneous in their search and representation methods (e.g. a rule learner, CN2 [Clark89]; a decision tree learner, C4.5 [Quinlan93]; an oblique decision tree learner, OC1 [Murthy93]; an instance-based learner, PEBLS [Cost93]; a k-nearest neighbor learner, ten domains and compared to several other meta-learning strategies.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
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Classify the node ' Learning roles: Behavioral diversity in robot teams. : This paper describes research investigating behavioral specialization in learning robot teams. Each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a task-achieving strategy using reinforcement learning. The agents learn individually to activate particular behavioral assemblages given their current situation and a reward signal. The experiments, conducted in robot soccer simulations, evaluate the agents in terms of performance, policy convergence, and behavioral diversity. The results show that in many cases, robots will automatically diversify by choosing heterogeneous behaviors. The degree of diversification and the performance of the team depend on the reward structure. When the entire team is jointly rewarded or penalized (global reinforcement), teams tend towards heterogeneous behavior. When agents are provided feedback individually (local reinforcement), they converge to identical policies.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node 'Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract: In this paper we give a completeness theorem of an inductive inference rule inverse entailment proposed by Muggleton. Our main result is that a hypothesis clause H can be derived from an example E under a background theory B with inverse entailment iff H subsumes E relative to B in Plotkin's sense. The theory B can be any clausal theory, and the example E can be any clause which is neither a tautology nor implied by B. The derived hypothesis H is a clause which is not always definite. In order to prove the result we give declarative semantics for arbitrary consistent clausal theories, and show that SB-resolution, which was originally introduced by Plotkin, is complete procedural semantics. The completeness is shown as an extension of the completeness theorem of SLD-resolution. We also show that every hypothesis H derived with saturant generalization, proposed by Rouveirol, must subsume E w.r.t. B in Buntine's sense. Moreover we show that saturant generalization can be obtained from inverse entailment by giving some restriction to its usage.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
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Classify the node ' What DaimlerBenz has learned as an industrial partner from the machine learning project Statlog. Working Notes for Applying Machine Learning in Practice: : Author of this paper was co-ordinator of the Machine Learning project StatLog during 1990-1993. This project was supported financially by the European Community. The main aim of StatLog was to evaluate different learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced different applications to Stat-Log among them fault diagnosis, letter and digit recognition, credit-scoring and prediction of the number of registered trucks. We have learned a lot of lessons from this project which have effected our application oriented research in the field of Machine Learning (ML) in Daimler-Benz. We have distinguished that, especially, more research is necessary to prepare the ML-algorithms to handle the real industrial and commercial applications. In this paper we describe, shortly, the Daimler-Benz applications in StatLog, we discuss shortcomings of the applied ML-algorithms and finally we outline the fields where we think further research is necessary.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
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Classify the node ' Fast equi-partitioning of rectangular domains using stripe decomposition. : This paper presents a fast algorithm that provides optimal or near optimal solutions to the minimum perimeter problem on a rectangular grid. The minimum perimeter problem is to partition a grid of size M N into P equal area regions while minimizing the total perimeter of the regions. The approach taken here is to divide the grid into stripes that can be filled completely with an integer number of regions . This striping method gives rise to a knapsack integer program that can be efficiently solved by existing codes. The solution of the knapsack problem is then used to generate the grid region assignments. An implementation of the algorithm partitioned a 1000 1000 grid into 1000 regions to a provably optimal solution in less than one second. With sufficient memory to hold the M N grid array, extremely large minimum perimeter problems can be solved easily.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
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Classify the node 'Structured Reachability Analysis for Markov Decision Processes: Recent research in decision theoretic planning has focussed on making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structured reachability analysis of MDPs that are suitable when an initial state (or set of states) is known. Using compact, structured representations of MDPs (e.g., Bayesian networks), our methods, which vary in the tradeoff between complexity and accuracy, produce structured descriptions of (estimated) reachable states that can be used to eliminate variables or variable values from the problem description, reducing the size of the MDP and making it easier to solve. One contribution of our work is the extension of ideas from GRAPHPLAN to deal with the distributed nature of action representations typically embodied within Bayes nets and the problem of correlated action effects. We also demonstrate that our algorithm can be made more complete by using k-ary constraints instead of binary constraints. Another contribution is the illustration of how the compact representation of reachability constraints can be exploited by several existing (exact and approximate) abstraction algorithms for MDPs.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node 'Is Consistency Harmful?: We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a new goal of hypothesis simplicity (Occam's razor). Instead of using simplicity as a goal, we have developed a novel approach that addresses consistency directly. In other words, our concept learner has the explicit goal of selecting the most appropriate degree of consistency with the training data. We begin this paper by exploring concept learning with less than perfect consistency. Next, we describe a system that can adapt its degree of consistency in response to feedback about predictive accuracy on test data. Finally, we present the results of initial experiments that begin to address the question of how tightly hypotheses should fit the training data for different problems.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
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Classify the node 'Meter as Mechanism: A Neural Network that Learns Metrical Patterns: One kind of prosodic structure that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of the timing of both music and speech show that the nested periodicities that define metrical structure can be quite noisy in time. What kind of system could produce or perceive such variable metrical timing patterns? And what would it take to be able to store and reproduce particular metrical patterns from long-term memory? We have developed a network of coupled oscillators that both produces and perceives patterns of pulses that conform to particular meters. In addition, beginning with an initial state with no biases, it can learn to prefer the particular meter that it has been previously exposed to. Meter is an abstract structure in time based on the periodic recurrence of pulses, that is, on equal time intervals between distinct phase zeros. From this point of view, the simplest meter is a regular metronome pulse. But often there appear meters with two or three (or rarely even more) nested periodicities with integral frequency ratios. A hierarchy of such metrical structures is implied in standard Western musical notation, where different levels of the metrical hierarchy are indicated by kinds of notes (quarter notes, half notes, etc.) and by the bars separating measures with an equal number of beats. For example, in a basic waltz-time meter, there are individual beats, all with the same spacing, grouped into sets of three, with every third one receiving a stronger accent at its onset. In this meter there is a hierarchy consisting of both a faster periodic cycle (at the beat level) and a slower one (at the measure level) that is 1/3 as fast, with its onset (or zero phase angle) coinciding with the zero phase angle of every third beat. This essentially temporal view of meter contrasts with the traditional symbol-string theories (such as Hayes, 1981 for speech and Lerdahl and Jackendoff, 1983 for music). Metrical systems, however they are defined, seem to underlie most of what we call music. Indeed, an expanded version of European musical notation is found to be practical for transcribing most music from around the world. That is, most forms of music employ nested periodic temporal patterns (Titon, Fujie, & Locke, 1996). Musical notation has' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
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Classify the node ' A qualitative framework for probabilistic inference. : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node 'Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,: The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node 'Importance Sampling: Technical Report No. 9805, Department of Statistics, University of Toronto Abstract. Simulated annealing | moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions | has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspect allows this method to perform acceptably even for high-dimensional problems, where finding good importance sampling distributions would otherwise be very difficult, while the use of importance weights ensures that the estimates found converge to the correct values as the number of annealing runs increases. This annealed importance sampling procedure resembles the second half of the previously-studied tempered transitions, and can be seen as a generalization of a recently-proposed variant of sequential importance sampling. It is also related to thermodynamic integration methods for estimating ratios of normalizing constants. Annealed importance sampling is most attractive when isolated modes are present, or when estimates of normalizing constants are required, but it may also be more generally useful, since its independent sampling allows one to bypass some of the problems of assessing convergence and autocorrelation in Markov chain samplers.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node ' Using smoothing spline anova to examine the relation of risk factors to the incidence and progression of diabetic retinopathy, : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node ' Learning from an automated training agent. : A learning agent employing reinforcement learning is hindered because it only receives the critic's sparse and weakly informative training information. We present an approach in which an automated training agent may also provide occasional instruction to the learner in the form of actions for the learner to perform. The learner has access to both the critic's feedback and the trainer's instruction. In the experiments, we vary the level of the trainer's interaction with the learner, from allowing the trainer to instruct the learner at almost every time step, to not allowing the trainer to respond at all. We also vary a parameter that controls how the learner incorporates the trainer's actions. The results show significant reductions in the average number of training trials necessary to learn to perform the task.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node 'How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications: This paper proposes a simple cost model for machine learning applications based on the notion of net present value. The model extends and unifies the models used in (Pazzani et al., 1994) and (Masand & Piatetsky-Shapiro, 1996). It attempts to answer the question "Should a given machine learning system now in the prototype stage be fielded?" The model's inputs are the system's confusion matrix, the cash flow matrix for the application, the cost per decision, the one-time cost of deploying the system, and the rate of return on investment. Like Provost and Fawcett's (1997) ROC convex hull method, the present model can be used for decision-making even when its input variables are not known exactly. Despite its simplicity, it has a number of non-trivial consequences. For example, under it the "no free lunch" theorems of learning theory no longer apply.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
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Classify the node 'LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study Machine Learning and Inference Laboratory: This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and represented in a declarative form. When needed for decision making, it should be efficiently transferred to a procedural form that is tailored to the specific decision making situation. Such an approach combines advantages of the declarative representation, which facilitates learning and incremental knowledge modification, and the procedural representation, which facilitates the use of knowledge for decision making. This approach also allows one to determine decision structures that may avoid attributes that unavailable or difficult to measure in any given situation. Experimental investigations of the system, FRD-1, have demonstrated that decision structures obtained via the declarative route often have not only higher predictive accuracy but are also are simpler than those learned directly from facts.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
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Classify the node ' A reversible jump sampler for autoregressive time series. : Technical Report CUED/F-INFENG/TR. 304 We use reversible jump Markov chain Monte Carlo (MCMC) methods (Green 1995) to address the problem of model order uncertainty in au-toregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for the new parameters in each move. Results are presented for both synthetic and audio time series.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node ' Issues in using function approximation for reinforcement learning. : Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Little, however, is understood about the theoretical properties of such combinations, and many researchers have encountered failures in practice. In this paper we identify a prime source of such failuresnamely, a systematic overestimation of utility values. Using Watkins' Q-Learning [18] as an example, we give a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail. Employing some of the most popular function approximators, we present experimental results which support the theoretical findings.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node ' Evolving sensors in environments of controlled complexity. : 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent energy environments" (LEE) models to define environments of carefully controlled complexity which allow us to state bounds for random and optimal behaviors that are independent of strategies for achieving the behaviors. Using LEE's analytic basis for defining environments, we then use neural networks (NNets) to model individuals and a steady - state genetic algorithm to model an evolutionary process shaping the NNets, in particular their sensors. Our experiments consider two types of "contact" and "ambient" sensors, and variants where the NNets are not allowed to learn, learn via error correction from internal prediction, and via reinforcement learning. We find that predictive learning, even when using a larger repertoire of the more sophisticated ambient sensors, provides no advantage over NNets unable to learn. However, reinforcement learning using a small number of crude contact sensors does provide a significant advantage. Our analysis of these results points to a tradeoff between the genetic "robustness" of sensors and their informativeness to a learning system.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
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Classify the node ' Kanazawa, Reasoning about Time and Probability, : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
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Classify the node ' "Genetic Programming Exploratory Power and the Discovery of Functions," : Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, an analysis of the causality of the crossover operator suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
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Classify the node ' A heuristic approach to the discovery of macro-operators. : The negative effect is naturally more significant in the more complex domain. The graph for the simple domain crosses the 0 line earlier than the complex domain. That means that learning starts to be useful with weight greater than 0.6 for the simple domain and 0.7 for the complex domain. As we relax the optimality requirement more s i g n i f i c a n t l y ( w i t h a W = 0.8), macro usage in the more complex domain becomes more advantageous. The purpose of the research described in this paper is to identify the parameters that effects deductive learning and to perform experiments systematically in order to understand the nature of those effects. The goal of this paper is to demonstrate the methodology of performing parametric experimental study of deductive learning. The example here include the study of two parameters: the point on the satisficing-optimizing scale that is used during the search carried out during problem solving time and during learning time. We showed that A*, which looks for optimal solutions, cannot benefit from macro learning but as the strategy comes closer to best-first (satisficing search), the utility of macros increases. We also demonstrated that deductive learners that learn offline by solving training problems are sensitive to the type of search used during the learning. We showed that in general optimizing search is best for learning. It generates macros that increase the quality solutions regardless of the search method used during problem solving. It also improves the efficiency for problem solvers that require a high level of optimality. The only drawback in using optimizing search is the increase in learning resources spent. We are aware of the fact that the results described here are not very surprising. The goal of the parametric study is not necessarily to find exciting results, but to obtain results, sometimes even previously known, in a controlled experimental environment. The work described here is only part of our research plan. We are currently in the process of extensive experimentation with all the parameters described here and also with others. We also intend to test the validity of the conclusions reached during the study by repeating some of the tests in several of the commonly known search problems. We hope that such systematic experimentation will help the research community to better understand the process of deductive learning and will serve as a demonstration of the experimental methodology that should be used in machine learning research.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 521
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Classify the node 'd d Code Scheduling for Multiple Instruction Stream Architectures: Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents our investigation into ways to modify MIMD architectures to allow them to extract the instruction level parallelism achieved by current superscalar and VLIW machines. A new architecture is proposed which utilizes the advantages of a multiple instruction stream design while addressing some of the limitations that have prevented MIMD architectures from performing ILP operation. A new code scheduling mechanism is described to support this new architecture by partitioning instructions across multiple processing elements in order to exploit this level of parallelism.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 529
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Classify the node 'GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: a collection of Fortran subroutines for binary, binomial, Poisson and Gamma data. We also show how to calculate Bayesian confidence intervals for SS ANOVA estimates.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 540
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Classify the node ' Sensitivities: an alternative to conditional probabilities for Bayesian belief networks. : We show an alternative way of representing a Bayesian belief network by sensitivities and probability distributions. This representation is equivalent to the traditional representation by conditional probabilities, but makes dependencies between nodes apparent and intuitively easy to understand. We also propose a QR matrix representation for the sensitivities and/or conditional probabilities which is more efficient, in both memory requirements and computational speed, than the traditional representation for computer-based implementations of probabilistic inference. We use sensitivities to show that for a certain class of binary networks, the computation time for approximate probabilistic inference with any positive upper bound on the error of the result is independent of the size of the network. Finally, as an alternative to traditional algorithms that use conditional probabilities, we describe an exact algorithm for probabilistic inference that uses the QR-representation for sensitivities and updates probability distributions of nodes in a network according to messages from the neigh bors.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 543
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Classify the node ' Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading, : A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi-variate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on out-of-sample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 572
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Classify the node ' A Case-based Approach to Reactive Control for Autonomous Robots. : We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive Robotic system) provides more flexible performance in novel environments, as well as overcoming a standard "hard" problem for reactive systems, the box canyon. Additionally, ACBARR is designed in a manner which is intended to remain as close to pure reactive control as possible. Higher level reasoning and memory functions are intentionally kept to a minimum. As a result, the new reasoning does not significantly slow the system down from pure reactive speeds.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 626
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Classify the node ' Combining rules and cases to learn case adaptation. : Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 630
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Classify the node 'Island Model Genetic Algorithms and Linearly Separable Problems: Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms; it is possible that Island Models are particular well suited to separable problems. We look at how Island Models can track multiple search trajectories using the infinite population models of the simple genetic algorithm. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 682
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Classify the node 'Voting for Schemata: The schema theorem states that implicit parallel search is behind the power of the genetic algorithm. We contend that chromosomes can vote, proportionate to their fitness, for candidate schemata. We maintain a population of binary strings and ternary schemata. The string population not only works on solving its problem domain, but it supplies fitness for the schema population, which indirectly can solve the original problem.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 704
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Classify the node ' Brain-Structured Networks That Perceive and Learn. : This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of such structures. The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g., houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation-discovery (feedback-guided growth of new links and nodes, subject to brain-like constraints (e.g., local receptive fields, global convergence-divergence). The information processing transforms discovered through generation are fine-tuned by feedback-guided reweight-ing of links. Some preliminary results are presented of brain-structured networks that learn to recognize simple objects (e.g., letters of the alphabet, cups, apples, bananas) through feedback-guided generation and reweighting. These show large improvements over networks that either lack brain-like structure or/and learn by reweighting of links alone.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 705
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Classify the node ' Task selection for a Multiscalar processor. : The Multiscalar architecture advocates a distributed processor organization and task-level speculation to exploit high degrees of instruction level parallelism (ILP) in sequential programs without impeding improvements in clock speeds. The main goal of this paper is to understand the key implications of the architectural features of distributed processor organization and task-level speculation for compiler task selection from the point of view of performance. We identify the fundamental performance issues to be: control ow speculation, data communication, data dependence speculation, load imbalance, and task overhead. We show that these issues are intimately related to a few key characteristics of tasks: task size, inter-task control ow, and inter-task data dependence. We describe compiler heuristics to select tasks with favorable characteristics. We report experimental results to show that the heuristics are successful in boosting overall performance by establishing larger ILP windows.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 707
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Classify the node ' Pattern analysis and synthesis in attractor neural networks. : The representation of hidden variable models by attractor neural networks is studied. Memories are stored in a dynamical attractor that is a continuous manifold of fixed points, as illustrated by linear and nonlinear networks with hidden neurons. Pattern analysis and synthesis are forms of pattern completion by recall of a stored memory. Analysis and synthesis in the linear network are performed by bottom-up and top-down connections. In the nonlinear network, the analysis computation additionally requires rectification nonlinearity and inner product inhibition between hidden neurons. One popular approach to sensory processing is based on generative models, which assume that sensory input patterns are synthesized from some underlying hidden variables. For example, the sounds of speech can be synthesized from a sequence of phonemes, and images of a face can be synthesized from pose and lighting variables. Hidden variables are useful because they constitute a simpler representation of the variables that are visible in the sensory input. Using a generative model for sensory processing requires a method of pattern analysis. Given a sensory input pattern, analysis is the recovery of the hidden variables from which it was synthesized. In other words, analysis and synthesis are inverses of each other. There are a number of approaches to pattern analysis. In analysis-by-synthesis, the synthetic model is embedded inside a negative feedback loop[1]. Another approach is to construct a separate analysis model[2]. This paper explores a third approach, in which visible-hidden pairs are embedded as attractive fixed points, or attractors, in the state space of a recurrent neural network. The attractors can be regarded as memories stored in the network, and analysis and synthesis as forms of pattern completion by recall of a memory. The approach is illustrated with linear and nonlinear network architectures. In both networks, the synthetic model is linear, as in principal' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 733
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Classify the node ' Dynamically adjusting concepts to accommodate changing contexts. : In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 736
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Classify the node 'On the Greediness of Feature Selection Algorithms: Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 763
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Classify the node ' Fast pruning using principal components. : We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, "Principal Components Pruning (PCP)", is based on principal component analysis of the node activations of successive layers of the network. It is simple, cheap to implement, and effective. It requires no network retraining, and does not involve calculating the full Hessian of the cost function. Only the weight and the node activity correlation matrices for each layer of nodes are required. We demonstrate the efficacy of the method on a regression problem using polynomial basis functions, and on an economic time series prediction problem using a two-layer, feedforward network.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 768
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Classify the node ' "The Predictability of Data Values", : Copyright 1997 IEEE. Published in the Proceedings of Micro-30, December 1-3, 1997 in Research Triangle Park, North Carolina. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions IEEE Service Center 445 Hoes Lane P.O. Box 1331 Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 834
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Classify the node 'Recombination Operator, its Correlation to the Fitness Landscape and Search Performance: The author reserves all other publication and other rights in association with the copyright in the thesis, and except as hereinbefore provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author's prior written permission.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 860
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Classify the node ' Learning Logical Exceptions in Chess. : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 862
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Classify the node ' A counter example to the stronger version of the binary tree hypothesis, : The paper describes a counter example to the hypothesis which states that a greedy decision tree generation algorithm that constructs binary decision trees and branches on a single attribute-value pair rather than on all values of the selected attribute will always lead to a tree with fewer leaves for any given training set. We show also that RELIEFF is less myopic than other impurity functions and that it enables the induction algorithm that generates binary decision trees to reconstruct optimal (the smallest) decision trees in more cases.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 867
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Classify the node ' Using Markov chains to analyze GAFOs. : Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 902
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Classify the node ' A genetic algorithm for 3-D path planning of a mobile robots, : This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 919
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Classify the node ' D.B. Leake. Modeling Case-based Planning for Repairing Reasoning Failures. : One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address the issues of the transferability of such models versus the specificity of the knowledge in them, the kinds of knowledge needed for self-modeling and how that knowledge is structured, and the evaluation of introspective reasoning systems. We present the ROBBIE system which implements a model of its planning processes to improve the planner in response to reasoning failures. We show how ROBBIE's hierarchical model balances model generality with access to implementation-specific details, and discuss the qualitative and quantitative measures we have used for evaluating its introspective component.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 923
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Classify the node ' PAC learning of one-dimensional patterns. : Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration of k points on the real line. Each instance is a configuration of n points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patterns P and Q resemble each other under the Hausdorff metric, if every point on one pattern is "close" to some point on the other pattern. We relate the concept class of geometric patterns to the landmark recognition problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 931
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Classify the node 'KnightCap: A chess program that learns by combining TD() with minimax search: In this paper we present TDLeaf(), a variation on the TD() algorithm that enables it to be used in conjunction with minimax search. We present some experiments in which our chess program, KnightCap, used TDLeaf() to learn its evaluation function while playing on the Free Ineternet Chess Server (FICS, fics.onenet.net). It improved from a 1650 rating to a 2100 rating in just 308 games and 3 days of play. We discuss some of the reasons for this success and also the relationship between our results and Tesauro's results in backgammon.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,002
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Classify the node ' On genetic algorithms. : We analyze the performance of a Genetic Algorithm (GA) we call Culling and a variety of other algorithms on a problem we refer to as Additive Search Problem (ASP). ASP is closely related to several previously well studied problems, such as the game of Mastermind and additive fitness functions. We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Culling is efficient on ASP, highly noise tolerant, and the best known approach in some regimes. Noisy ASP is the first problem we are aware of where a Genetic Type Algorithm bests all known competitors. Standard GA's, by contrast, perform much more poorly on ASP than hillclimbing and other approaches even though the Schema theorem holds for ASP. We generalize ASP to k-ASP to study whether GA's will achieve `implicit parallelism' in a problem with many more schemata. GA's fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a Mean Field Theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GA's can beat competing methods.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,004
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Classify the node ' Limits of Instruction-Level Parallelism, : This paper examines the limits to instruction level parallelism that can be found in programs, in particular the SPEC95 benchmark suite. Apart from using a more recent version of the SPEC benchmark suite, it differs from earlier studies in removing non-essential true dependencies that occur as a result of the compiler employing a stack for subroutine linkage. This is a subtle limitation to parallelism that is not readily evident as it appears as a true dependency on the stack pointer. Other methods can be used that do not employ a stack to remove this dependency. In this paper we show that its removal exposes far more parallelism than has been seen previously. We refer to this type of parallelism as "parallelism at a distance" because it requires impossibly large instruction windows for detection. We conclude with two observations: 1) that a single instruction window characteristic of superscalar machines is inadequate for detecting parallelism at a distance; and 2) in order to take advantage of this parallelism the compiler must be involved, or separate threads must be explicitly programmed.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,022
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Classify the node ' Operations for learning with graphical models. : This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 1,024
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Classify the node ' Residual q-learning aplied to visual attention. : Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is treated as a dynamically allocatable resource, but requires a more refined visual attention mechanism. We demonstrate that reinforcement learning (RL) significantly improves the performance of foveal visual attention, and of the overall vision system, for the task of model based target recognition. A simulated foveal vision system is shown to classify targets with fewer fixations by learning strategies for the acquisition of visual information relevant to the task, and learning how to generalize these strategies in ambiguous and unexpected scenario conditions.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,041
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Classify the node ' A benchmark for classifier learning. : Although many algorithms for learning from examples have been developed and many comparisons have been reported, there is no generally accepted benchmark for classifier learning. The existence of a standard benchmark would greatly assist such comparisons. Sixteen dimensions are proposed to describe classification tasks. Based on these, thirteen real-world and synthetic datasets are chosen by a set covering method from the UCI Repository of machine learning databases to form such a benchmark.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,071
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Classify the node ' (1995) Discretization of continuous attributes using ReliefF, : Instead of myopic impurity functions, we propose the use of Reli-efF for heuristic guidance of inductive learning algorithms. The basic algoritm RELIEF, developed by Kira and Rendell (Kira and Rendell, 1992a;b), is able to efficiently solve classification problems involving highly dependent attributes, such as parity problems. However, it is sensitive to noise and is unable to deal with incomplete data, multi-class, and regression problems (continuous class). We have extended RELIEF in several directions. The extended algorithm ReliefF is able to deal with noisy and incomplete data, can be used for multiclass problems, and its regressional variant RReliefF can deal with regression problems. Another area of application is inductive logic programming (ILP) where, instead of myopic measures, ReliefF can be used to estimate the utility of literals during the theory construction.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,098
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Classify the node ' On learning conjunctions with malicious noise. : We show how to learn monomials in the presence of malicious noise, when the underlined distribution is a product distribution. We show that our results apply not only to product distributions but to a wide class of distributions.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,103
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Classify the node ' Representing and restructuring domain theories: A constructive induction approach. : Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,111
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Classify the node 'MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-temporal resolutions; (b) lack of accuracy of the experimental paradigm used for EEG complementary studies. Images (Posner & Raichle, 1994) is an excellent introduction to interdisciplinary research in cognitive and imaging science. Well written and illustrated, it presents concepts in a manner well suited both to the layman/undergraduate and to the technical nonexpert/graduate student and postdoctoral researcher. Many, not all, people involved in interdisciplinary neuroscience research agree with the P & R's statements on page 33, on the importance of recognizing emergent properties of brain function from assemblies of neurons. It is clear from the sparse references that this book was not intended as a standalone review of a broad field. There are some aws in the scientific development, but this must be expected in such a pioneering venture. P & R hav e proposed many cognitive mechanisms deserving further study with imaging tools yet to be developed which can yield better spatial-temporal resolutions.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,113
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Classify the node ' An inductive learning approach to prognostic prediction. : This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may be only a lower bound on the "right answer." This approach is augmented with a feature selection method that chooses an appropriate feature set within the context of the linear programming generalizer. Computational results in the field of breast cancer prognosis are shown. A straightforward translation of the prediction method to an artificial neural network model is also proposed.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,136
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Classify the node ' Using marker-based genetic encoding of neural networks to evolve finite-state behaviour. : A new mechanism for genetic encoding of neural networks is proposed, which is loosely based on the marker structure of biological DNA. The mechanism allows all aspects of the network structure, including the number of nodes and their connectivity, to be evolved through genetic algorithms. The effectiveness of the encoding scheme is demonstrated in an object recognition task that requires artificial creatures (whose behaviour is driven by a neural network) to develop high-level finite-state exploration and discrimination strategies. The task requires solving the sensory-motor grounding problem, i.e. developing a functional understanding of the effects that a creature's movement has on its sensory input.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,139
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Classify the node ' A simple randomized quantization algorithm for neural network pattern classifiers. : This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomized thermometer code generation technique can result in quantized datasets that when used to train simple perceptrons, can yield generalization on test data that is substantially better than that obtained with their unquantized counterparts.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,142
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Classify the node ' Reinforcement Learning: A Survey. : This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,148
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Classify the node 'learning easier tasks. More work is necessary in order to determine more precisely the relationship: We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,161
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Classify the node ' Markov chain Monte Carlo in practice: A roundtable discussion. : Markov chain Monte Carlo (MCMC) methods make possible the use of flexible Bayesian models that would otherwise be computationally infeasible. In recent years, a great variety of such applications have been described in the literature. Applied statisticians who are new to these methods may have several questions and concerns, however: How much effort and expertise are needed to design and use a Markov chain sampler? How much confidence can one have in the answers that MCMC produces? How does the use of MCMC affect the rest of the model-building process? At the Joint Statistical Meetings in August, 1996, a panel of experienced MCMC users discussed these and other issues, as well as various "tricks of the trade". This paper is an edited recreation of that discussion. Its purpose is to offer advice and guidance to novice users of MCMC - and to not-so-novice users as well. Topics include building confidence in simulation results, methods for speeding and assessing convergence, estimating standard errors, identification of models for which good MCMC algorithms exist, and the current state of software development.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 1,164
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Classify the node ' (1995) Constructive Algorithms for Hierachical Mixtures of Experts. : We present two additions to the hierarchical mixture of experts (HME) architecture. We view the HME as a tree structured classifier. Firstly, by applying a likelihood splitting criteria to each expert in the HME we "grow" the tree adaptively during training. Secondly, by considering only the most probable path through the tree we may "prune" branches away, either temporarily, or permanently if they become redundant. We demonstrate results for the growing and pruning algorithms which show significant speed ups and more efficient use of parameters over the conventional algorithms in discriminating between two interlocking spirals and classifying 8-bit parity patterns.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,175
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Classify the node ' Avoiding overfitting with BP-SOM. : Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of gen-eralisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-som, to avoid overfitting. bp-som is a hybrid neural network which combines a multi-layered feed-forward network (mfn) with Kohonen's self-organising maps (soms). During training, supervised back-propagation learning and unsupervised som learning cooperate in finding adequate hidden-layer representations. We show that bp-som outperforms standard backpropagation, and also back-propagation with a weight decay when dealing with the problem of overfitting. In addition, we show that bp-som succeeds in preserving generalisation performance under hidden-unit pruning, where both other methods fail.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,181
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Classify the node ' Mutation rates as adaptations. : In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,185
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Classify the node 'Dynamic Non-Bayesian Decision Making: The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change arbitrarily, and the feedback/reward function is initially unknown. The agent is not Bayesian, that is he does not form a prior probability neither on the state selection strategy of Nature, nor on his reward function. A policy for the agent is a function which assigns an action to every history of observations and actions. Two basic feedback structures are considered. In one of them the perfect monitoring case the agent is able to observe the previous environment state as part of his feedback, while in the other the imperfect monitoring case all that is available to the agent is the reward obtained. Both of these settings refer to partially observable processes, where the current environment state is unknown. Our main result refers to the competitive ratio criterion in the perfect monitoring case. We prove the existence of an efficient stochastic policy that ensures that the competitive ratio is obtained at almost all stages with an arbitrarily high probability, where efficiency is measured in terms of rate of convergence. It is further shown that such an optimal policy does not exist in the imperfect monitoring case. Moreover, it is proved that in the perfect monitoring case there does not exist a deterministic policy that satisfies our long run optimality criterion. In addition, we discuss the maxmin criterion and prove that a deterministic efficient optimal strategy does exist in the imperfect monitoring case under this criterion. Finally we show that our approach to long-run optimality can be viewed as qualitative, which distinguishes it from previous work in this area.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,186
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Classify the node ' The GP-Music System: Interactive Genetic Programming for Music Composition, : Technical Report CSRP-98-13 Abstract In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming, and its extensions aimed at making the system fully automated. The basic GP-system works by using a genetic programming algorithm, a small set of functions for creating musical sequences, and a user interface which allows the user to rate individual sequences. With this user interactive technique it was possible to generate pleasant tunes over runs of 20 individuals over 10 generations. As the user is the bottleneck in interactive systems, the system takes rating data from a users run and uses it to train a neural network based automatic rater, or auto rater, which can replace the user in bigger runs. Using this auto rater we were able to make runs of up to 50 generations with 500 individuals per generation. The best of run pieces generated by the auto raters were pleasant but were not, in general, as nice as those generated in user interactive runs.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,188
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Classify the node 'Predictive Robot Control with Neural Networks: Neural controllers are able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot manipulator above an object which is arbitrary placed on a table. The desired camera-joint mapping is approximated by feedforward neural networks. However, if the object is moving, the manipulator lags behind because of the required time to preprocess the visual information and to move the manipulator. Through the use of time derivatives of the position of the object and of the manipulator, the controller can inherently predict the next position of the object. In this paper several `predictive' controllers are proposed, and successfully applied to track a moving object.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,212
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Classify the node 'Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment: The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,233
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Classify the node ' "An analysis of bayesian classifiers," : In this paper we present an average-case analysis of the Bayesian classifier, a simple probabilistic induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, Boolean attributes that are independent of each other and that follow a single distribution, and the absence of attribute noise. We first calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions; we then use this expression to compute the probability of correct classification over the space of instances. The analysis takes into account the number of training instances, the number of relevant and irrelevant attributes, the distribution of these attributes, and the level of class noise. In addition, we explore the behavioral implications of the analysis by presenting predicted learning curves for a number of artificial domains. We also give experimental results on these domains as a check on our reasoning. Finally, we discuss some unresolved questions about the behavior of Bayesian classifiers and outline directions for future research. Note: Without acknowledgements and references, this paper fits into 12 pages with dimensions 5.5 inches fi 7.5 inches using 12 point LaTeX type. However, we find the current format more desirable. We have not submitted the paper to any other conference or journal.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,237
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Classify the node ' M.L. (1996) Design by Interactive Exploration Using Memory-Based Techniques. : One of the characteristics of design is that designers rely extensively on past experience in order to create new designs. Because of this, memory-based techniques from artificial intelligence, which help store, organise, retrieve, and reuse experiential knowledge held in memory, are good candidates for aiding designers. Another characteristic of design is the phenomenon of exploration in the early stages of design configuration. A designer begins with an ill-structured, partially defined, problem specification, and through a process of exploration gradually refines and modifies it as his/her understanding of the problem improves. In this paper we describe demex, an interactive computer-aided design system that employs memory-based techniques to help its users explore the design problems they pose to the system, in order to acquire a better understanding of the requirements of the problems. demex has been applied in the domain of structural design of buildings.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 1,265
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Classify the node 'Learning High Utility Rules by Incorporating Search Control Guidance Committee: ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 1,266
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Classify the node 'A Neural Network Based Head Tracking System: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to the neural network consist of normalized luminance and chrominance images and motion information from frame differences. Subsampled images are also used to provide scale invariance. During the online training phase, the neural network rapidly adjusts the input weights depending upon the reliability of the different channels in the surrounding environment. This quick adaptation allows the system to robustly track a head even when other objects are moving within a cluttered background.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,286
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Classify the node 'Learning in design: From Characterizing Dimensions to Working Systems: The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Central to the development of the concepts discussed in this paper is their use in future projects and the recording of their successes, limitations, and failures.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 1,304
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Classify the node 'Fast Online Q(): Q()-learning uses TD()-methods to accelerate Q-learning. The update complexity of previous online Q() implementations based on lookup-tables is bounded by the size of the state/action space. Our faster algorithm's update complexity is bounded by the number of actions. The method is based on the observation that Q-value updates may be postponed until they are needed.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,331
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Classify the node ' Inducing logic programs without explicit negative examples. : This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate and each training input is assumed to be accompanied by all of its legal outputs. Any other outputs generated by an incomplete program implicitly represent negative examples; however, large numbers of ground negative examples never need to be generated. This method has been incorporated into two ILP systems, Chillin and IFoil, both of which use intensional background knowledge. Tests on two natural language acquisition tasks, case-role mapping and past-tense learning, illustrate the advantages of the approach.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,367
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Classify the node ' Induction of Recursive Bayesian Classifiers In Proc. : We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classifier with that of (a) Bayesian network classifiers that incorporate all attributes, (b) selective and non-selective naive Bayesian classifiers, and (c) the decision-tree algorithm C4.5. With respect to (a), we show that our approach generates networks that are computationally simpler to evaluate but display comparable predictive accuracy. With respect to (b), we show that the selective Bayesian network classifier performs significantly better than both versions of the naive Bayesian classifier on almost all databases studied, and hence is an enhancement of the naive method. With respect to (c), we show that the selective Bayesian network classifier displays comparable behavior.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 1,372
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Classify the node 'PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,378
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Classify the node ' Toward an ideal trainer. : This paper appeared in 1994 in Machine Learning, 15 (3): 251-277. Abstract This paper demonstrates how the nature of the opposition during training affects learning to play two-person, perfect information board games. It considers different kinds of competitive training, the impact of trainer error, appropriate metrics for post-training performance measurement, and the ways those metrics can be applied. The results suggest that teaching a program by leading it repeatedly through the same restricted paths, albeit high quality ones, is overly narrow preparation for the variations that appear in real-world experience. The results also demonstrate that variety introduced into training by random choice is unreliable preparation, and that a program that directs its own training may overlook important situations. The results argue for a broad variety of training experience with play at many levels. This variety may either be inherent in the game or introduced deliberately into the training. Lesson and practice training, a blend of expert guidance and knowledge-based, self-directed elaboration, is shown to be particularly effective for learning during competition.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,406
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Classify the node ' (1996) "Bayesian curve fitting using multivariate normal mixtures," : Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multi-dimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression functions have features similar to generalised kernel regression estimates, but the formal analysis addresses problems of multivariate smoothing parameter estimation and the assessment of uncertainties about regression functions naturally. Computations are based on multi-dimensional versions of existing Markov chain simulation analysis of univariate Dirichlet mixture models.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 1,415
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Classify the node ' Extending theory refinement to m-of-n rules. : In recent years, machine learning research has started addressing a problem known as theory refinement. The goal of a theory refinement learner is to modify an incomplete or incorrect rule base, representing a domain theory, to make it consistent with a set of input training examples. This paper presents a major revision of the Either propositional theory refinement system. Two issues are discussed. First, we show how run time efficiency can be greatly improved by changing from a exhaustive scheme for computing repairs to an iterative greedy method. Second, we show how to extend Either to refine M-of-N rules. The resulting algorithm, Neither (New Either), is more than an order of magnitude faster and produces significantly more accurate results with theories that fit the M-of-N format. To demonstrate the advantages of Neither, we present experimental results from two real-world domains.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,429
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Classify the node ' Strongly typed genetic programming in evolving cooperation strategies. : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,466
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Classify the node ' Learning to achieve goals. : Temporal difference methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal difference methods, such as Q learning, can be applied to this problem, they do not take advantage of its special structure. This paper presents the DG-learning algorithm, which learns efficiently to achieve dynamically changing goals and exhibits good knowledge transfer between goals. In addition, this paper shows how traditional relaxation techniques can be applied to the problem. Finally, experimental results are given that demonstrate the superiority of DG learning over Q learning in a moderately large, synthetic, non-deterministic domain.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,522
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Classify the node ' "Robot Juggling: An Implementation of Memory-Based Learning," : This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local model - ing approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its pre diction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements dur ing explo - ration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real - time learning of the task within 40 to 100 trials. * Address of both authors: Massachusetts Institute of Technology, The Artificial Intelligence Laboratory & The Department of Brain and Cognitive Sciences, 545 Technology Square, Cambride, MA 02139, USA. Email: [email protected], [email protected]. Support was provided by the Air Force Office of Sci entific Research and by Siemens Cor pora tion. Support for the first author was provided by the Ger man Scholar ship Foundation and the Alexander von Hum boldt Founda tion. Support for the second author was provided by a Na tional Sci ence Foundation Pre sidential Young Investigator Award. We thank Gideon Stein for im ple ment ing the first version of LWR on the i860 microprocessor, and Gerrie van Zyl for build ing the devil stick robot and implementing the first version of devil stick learning.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Case Based
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cora
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train
| 1,536
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Classify the node ' Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generation of multiple sequence alignments. The results of studies attempting to infer appropriate parameter constraints for the generation of de novo HMMs for globin, kinase, aspartic acid protease, and ribonuclease H sequences by both the SAM and HMMER methods are described.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,599
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Classify the node 'Spline Smoothing For Bivariate Data With Applications To Association Between Hormones: ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Neural Networks
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cora
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train
| 1,631
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Classify the node ' How to dynamically merge markov decision processes. : We are frequently called upon to perform multiple tasks that compete for our attention and resource. Often we know the optimal solution to each task in isolation; in this paper, we describe how this knowledge can be exploited to efficiently find good solutions for doing the tasks in parallel. We formulate this problem as that of dynamically merging multiple Markov decision processes (MDPs) into a composite MDP, and present a new theoretically-sound dynamic programming algorithm for finding an optimal policy for the composite MDP. We analyze various aspects of our algorithm and Every day, we are faced with the problem of doing multiple tasks in parallel, each of which competes for our attention and resource. If we are running a job shop, we must decide which machines to allocate to which jobs, and in what order, so that no jobs miss their deadlines. If we are a mail delivery robot, we must find the intended recipients of the mail while simultaneously avoiding fixed obstacles (such as walls) and mobile obstacles (such as people), and still manage to keep ourselves sufficiently charged up. Frequently we know how to perform each task in isolation; this paper considers how we can take the information we have about the individual tasks and combine it to efficiently find an optimal solution for doing the entire set of tasks in parallel. More importantly, we describe a theoretically-sound algorithm for doing this merging dynamically; new tasks (such as a new job arrival at a job shop) can be assimilated online into the solution being found for the ongoing set of simultaneous tasks. illustrate its use on a simple merging problem.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,647
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Classify the node ' Evolving Optimal Populations with XCS Classifier Systems, : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Reinforcement Learning
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cora
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train
| 1,648
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Classify the node ' (1997b) Probabilistic Modeling for Combinatorial Optimization, : Probabilistic models have recently been utilized for the optimization of large combinatorial search problems. However, complex probabilistic models that attempt to capture inter-parameter dependencies can have prohibitive computational costs. The algorithm presented in this paper, termed COMIT, provides a method for using probabilistic models in conjunction with fast search techniques. We show how COMIT can be used with two very different fast search algorithms: hillclimbing and Population-based incremental learning (PBIL). The resulting algorithms maintain many of the benefits of probabilistic modeling, with far less computational expense. Extensive empirical results are provided; COMIT has been successfully applied to jobshop scheduling, traveling salesman, and knapsack problems. This paper also presents a review of probabilistic modeling for combi natorial optimization.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Genetic Algorithms
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cora
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train
| 1,689
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Classify the node ' Metric Entropy and Minimax Risk in Classification, : We apply recent results on the minimax risk in density estimation to the related problem of pattern classification. The notion of loss we seek to minimize is an information theoretic measure of how well we can predict the classification of future examples, given the classification of previously seen examples. We give an asymptotic characterization of the minimax risk in terms of the metric entropy properties of the class of distributions that might be generating the examples. We then use these results to characterize the minimax risk in the special case of noisy two-valued classification problems in terms of the Assouad density and the' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,691
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Classify the node 'Feature Generation for Sequence Categorization: The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not known and straightforward representations are often far from optimal. We propose a feature generation method (called FGEN) that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. We show empirically that the representation computed by FGEN improves the accuracy of two commonly used learning systems (C4.5 and Ripper) when the new features are added to existing representations of sequence data. We show the superiority of FGEN across a range of tasks selected from three domains: DNA sequences, Unix command sequences, and English text.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,714
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Classify the node ' (1996c) Feedback Models: Interpretation and Discovery. : ' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Probabilistic Methods
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cora
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train
| 1,732
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Classify the node ' Profile-driven instruction level parallel scheduling with application to super blocks. : Code scheduling to exploit instruction level parallelism (ILP) is a critical problem in compiler optimization research, in light of the increased use of long-instruction-word machines. Unfortunately, optimum scheduling is com-putationally intractable, and one must resort to carefully crafted heuristics in practice. If the scope of application of a scheduling heuristic is limited to basic blocks, considerable performance loss may be incurred at block boundaries. To overcome this obstacle, basic blocks can be coalesced across branches to form larger regions such as super blocks. In the literature, these regions are typically scheduled using algorithms that are either oblivious to profile information (under the assumption that the process of forming the region has fully utilized the profile information), or use the profile information as an addendum to classical scheduling techniques. We believe that even for the simple case of linear code regions such as super blocks, additional performance improvement can be gained by utilizing the profile information in scheduling as well. We propose a general paradigm for converting any profile-insensitive list sched-uler to a profile-sensitive scheduler. Our technique is developed via a theoretical analysis of a simplified abstract model of the general problem of profile-driven scheduling over any acyclic code region, yielding a scoring measure for ranking branch instructions. The ranking digests the profile information and has the useful property that scheduling with respect to rank is provably good for minimizing the expected completion time of the region, within the limits of the abstraction. While the ranking scheme is computation-ally intractable in the most general case, it is practicable for super blocks and suggests the heuristic that we present in this paper for profile-driven scheduling of super blocks. Experiments show that our heuristic offers substantial performance improvement over prior methods on a range of integer benchmarks and several machine models.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Rule Learning
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cora
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train
| 1,745
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Classify the node ' A note on learning from multiple--instance examples. : We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all concepts learnable in the usual 2-sided random noise model plus others such as the parity function, are learnable from multiple-instance examples. We also describe a more efficient (and somewhat technically more involved) reduction to the Statistical-Query model that results in a polynomial-time algorithm for learning axis-parallel rectangles with sample complexity ~ O(d 2 r=* 2 ), saving roughly a factor of r over the results of Auer et al. (1997).' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
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Theory
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cora
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train
| 1,781
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