Split (1)

train · 203k rows

node_id int64 0 76.9k	label int64 0 39	text stringlengths 13 124k	neighbors listlengths 0 3.32k	mask stringclasses 4 values
394	2	Title: Prior Information and Generalized Questions Abstract: This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. This research is sponsored by a grant from National Science Found...	[ 125, 133, 608 ]	Train
395	1	Title: Evolving Graphs and Networks with Edge Encoding: Preliminary Report Abstract: We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators ...	[ 163, 189, 191 ]	Validation
396	5	Title: Geometric Comparison of Classifications and Rule Sets* Abstract: We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class description...	[ 378, 478 ]	Train
397	2	Title: Truth-from-Trash Learning and the Mobot Abstract: As natural resources become less abundant, we naturally become more interested in, and more adept at utilisation of waste materials. In doing this we are bringing to bear a ploy which is of key importance in learning \| or so I argue in this paper. In the `Truth...	[ 659, 747, 2346 ]	Validation
398	3	Title: Axioms of Causal Relevance Abstract: This paper develops axioms and formal semantics for statements of the form "X is causally irrelevant to Y in context Z," which we interpret to mean "Changing X will not affect Y if we hold Z constant." The axiomization of causal irrelevance is contrasted with the axiomizati...	[ 248, 419, 776 ]	Train
399	2	Title: Representing and Learning Visual Schemas in Neural Networks for Scene Analysis Abstract: Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arise...	[ 15, 427, 1250, 1251 ]	Train
400	6	Title: Learning Algorithms with Applications to Robot Navigation and Protein Folding Abstract: Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arises...	[ 14, 258, 555, 2354, 2360 ]	Test
401	3	Title: Learning Limited Dependence Bayesian Classifiers Abstract: We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning...	[ 442, 577, 632, 2462 ]	Validation
402	1	Title: The Evolutionary Cost of Learning Abstract: Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionar...	[ 163, 219, 403, 538, 681 ]	Train
403	1	Title: Landscapes, Learning Costs and Genetic Assimilation. Abstract: The evolution of a population can be guided by phenotypic traits acquired by members of that population during their lifetime. This phenomenon, known as the Baldwin Effect, can speed the evolutionary process as traits that are initially acquired be...	[ 402, 2104, 2302, 2309 ]	Validation
404	2	Title: EE380L:Neural Networks for Pattern Recognition POp Trees under the guidance of Abstract: Decision Trees have been widely used for classification/regression tasks. They are relatively much faster to build as compared to Neural Networks and are understandable by humans. In normal decision trees, based on the i...	[ 102 ]	Test
405	2	Title: Finite State Machines and Recurrent Neural Networks Automata and Dynamical Systems Approaches Abstract: Decision Trees have been widely used for classification/regression tasks. They are relatively much faster to build as compared to Neural Networks and are understandable by humans. In normal decision trees, b...	[ 753, 1285, 1592 ]	Train
406	2	Title: Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization Abstract: The fundamental backpropagation (BP) algorithm for training artificial neural networks is cast as a deterministic nonmonotone perturbed gradient method . Under certain natural assumptions, such as the series of learning ...	[ 230, 311, 427, 2307 ]	Train
407	2	Title: Constructing Deterministic Finite-State Automata in Recurrent Neural Networks Abstract: Recurrent neural networks that are trained to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the in...	[ 512, 1298, 1763, 1875, 2439 ]	Test
408	2	Title: Constructing Deterministic Finite-State Automata in Recurrent Neural Networks Abstract: Report SYCON-93-09 Recent Results on Lyapunov-theoretic Techniques for Nonlinear Stability ABSTRACT This paper presents a Converse Lyapunov Function Theorem motivated by robust control analysis and design. Our result is bas...	[ 630 ]	Train
409	2	Title: Extraction of Rules from Discrete-Time Recurrent Neural Networks Abstract: The extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectio...	[ 753, 1298, 1606, 2582 ]	Validation
410	4	Title: High-Performance Job-Shop Scheduling With A Time-Delay TD() Network Abstract: Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating...	[ 2, 82, 298, 305, 565 ]	Validation
411	2	Title: POWER OF NEURAL NETS Abstract: Report SYCON-91-11 ABSTRACT This paper deals with the simulation of Turing machines by neural networks. Such networks are made up of interconnections of synchronously evolving processors, each of which updates its state according to a "sigmoidal" linear combination of the previou...	[ 512, 536, 1470, 1600, 1891, 2232, 2582, 2594 ]	Train
412	4	Title: The Influence of Domain Properties on the Performance of Real-Time Search Algorithms Abstract: This research is sponsored by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and ...	[ 552, 688 ]	Train
413	2	Title: Convergence Rates of Approximation by Translates Abstract: In this paper we consider the problem of approximating a function belonging to some function space by a linear combination of n translates of a given function G. Using a lemma by Jones (1990) and Barron (1991) we show that it is possible to define func...	[ 611 ]	Train
414	0	Title: Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to Abstract: University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's ...	[ 440, 908, 1174, 1442, 1445, 1877 ]	Train
415	1	Title: Competitive Environments Evolve Better Solutions for Complex Tasks Abstract: University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar prob...	[ 163, 188, 209, 523, 712, 789, 995, 1737, 1790, 1832, 1836, 2103, 2353, 2664 ]	Train
416	3	Title: A note on convergence rates of Gibbs sampling for nonparametric mixtures Abstract: We consider a mixture model where the mixing distribution is random and is given a Dirichlet process prior. We describe the general structure of two Gibbs sampling algorithms that are useful for approximating Bayesian inferences...	[ 137, 138, 1713, 1913, 2510 ]	Train
417	5	Title: Constructing Intermediate Concepts by Decomposition of Real Functions Abstract: In learning from examples it is often useful to expand an attribute-vector representation by intermediate concepts. The usual advantage of such structuring of the learning problem is that it makes the learning easier and improves t...	[ 317, 508 ]	Validation
418	6	Title: Heterogeneous Uncertainty Sampling for Supervised Learning Abstract: Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One p...	[ 135, 479, 740, 1198, 1269, 1312 ]	Train
419	3	Title: On the Testability of Causal Models with Latent and Instrumental Variables Abstract: Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality constraints on the observed distribution. This paper derives a general fo...	[ 248, 260, 398, 1326, 1527, 1747 ]	Test
420	2	Title: Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals Abstract: Bayesian confidence intervals of a smoothing spline are often used to distinguish two curves. In this paper, we provide an asymptotic formula for sample size calculations based on Bayesian confidence intervals. Appr...	[ 10, 192, 193, 439, 519, 2214, 2590 ]	Train
421	6	Title: Improved Boosting Algorithms Using Confidence-rated Predictions Abstract: We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this s...	[ 255 ]	Validation
422	1	Title: Genetic Self-Learning Abstract: Evolutionary Algorithms are direct random search algorithms which imitate the principles of natural evolution as a method to solve adaptation (learning) tasks in general. As such they have several features in common which can be observed on the genetic and phenotypic level of li...	[ 163, 1069, 1685, 1691 ]	Train
423	3	Title: LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE Abstract: We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (...	[ 62, 557, 558, 1290, 1934, 2425 ]	Test
424	6	Title: Validation of Voting Committees Abstract: This paper contains a method to bound the test errors of voting committees with members chosen from a pool of trained classifiers. There are so many prospective committees that validating them directly does not achieve useful error bounds. Because there are fewer class...	[ 571 ]	Test
425	4	Title: Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil Abstract: An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n...	[ 85, 565 ]	Train
426	5	Title: Rule Induction with CN2: Some Recent Improvements Abstract: The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as ...	[ 29, 303, 318, 335, 836, 937, 1061, 1187, 1275, 1486, 1528, 1576, 2126, 2369, 2431 ]	Validation
427	2	Title: Book Review Introduction to the Theory of Neural Computation Reviewed by: 2 Abstract: Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impr...	[ 18, 146, 201, 202, 205, 230, 250, 304, 312, 350, 353, 391, 399, 406, 461, 477, 493, 494, 526, 561, 584, 587, 610, 658, 665, 674, 696, 698, 823, 916, 946, 954, 1037, 1103, 1274, 1283, 1284, 1318, 1340, 1399, 1405, 1488, 1547, 1577,...	Train
428	5	Title: Selective Eager Execution on the PolyPath Architecture Abstract: Control-flow misprediction penalties are a major impediment to high performance in wide-issue superscalar processors. In this paper we present Selective Eager Execution (SEE), an execution model to overcome mis-speculation penalties by executing ...	[ 184, 302, 432, 433, 598 ]	Test
429	3	Title: Classifiers: A Theoretical and Empirical Study Abstract: This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm and the several matu...	[ 29, 1290, 1514 ]	Test
430	6	Title: Irrelevant Features and the Subset Selection Problem Abstract: We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning ...	[ 52, 89, 119, 172, 177, 208, 223, 236, 256, 381, 524, 547, 632, 634, 635, 651, 683, 686, 1020, 1165, 1207, 1270, 1284, 1568, 1569, 1617, 1637, 1698, 1792, 2033, 2137, 2197, 2343, 2487, 2557, 2593 ]	Test
431	6	Title: Rule-based Machine Learning Methods for Functional Prediction Abstract: We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision...	[ 156, 1608, 2137 ]	Train
432	5	Title: Limited Dual Path Execution Abstract: This work presents a hybrid branch predictor scheme that uses a limited form of dual path execution along with dynamic branch prediction to improve execution times. The ability to execute down both paths of a conditional branch enables the branch penalty to be minimized; h...	[ 165, 184, 302, 428, 433, 598 ]	Train
433	5	Title: Threaded Multiple Path Execution Abstract: This paper presents Threaded Multi-Path Execution (TME), which exploits existing hardware on a Simultaneous Multi-threading (SMT) processor to speculatively execute multiple paths of execution. When there are fewer threads in an SMT processor than hardware contexts, t...	[ 158, 184, 428, 432 ]	Test
434	0	Title: Computational Learning in Humans and Machines Abstract: In this paper we review research on machine learning and its relation to computational models of human learning. We focus initially on concept induction, examining five main approaches to this problem, then consider the more complex issue of learning sequ...	[ 597, 1339, 2473 ]	Train
435	2	Title: Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to Abstract: The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs,...	[ 0, 8, 14, 258, 751 ]	Train
436	6	Title: Pattern Theoretic Knowledge Discovery Abstract: Future research directions in Knowledge Discovery in Databases (KDD) include the ability to extract an overlying concept relating useful data. Current limitations involve the search complexity to find that concept and what it means to be "useful." The Pattern The...	[ 635 ]	Train
437	2	Title: A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions Abstract: Prerequisites. An understanding of the dynamic programming (edit distance) approach to pairwise sequence alignment is useful for parts 1.3, 1.4, and 2. Also, familiarity with the use of Internet ...	[ 14 ]	Test
438	6	Title: A System for Induction of Oblique Decision Trees Abstract: This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tr...	[ 21, 79, 96, 227, 296, 391, 550, 607, 616, 692, 710, 720 ]	Train
439	2	Title: Adaptive tuning of numerical weather prediction models: Randomized GCV in three and four dimensional data assimilation Abstract: This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good obl...	[ 97, 192, 388, 420 ]	Validation
440	4	Title: Hierarchical Explanation-Based Reinforcement Learning Abstract: Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with the generalization ability of Explanation-Based Learning (EBL) (Di-...	[ 367, 414, 562 ]	Train
441	0	Title: BRACE: A Paradigm For the Discretization of Continuously Valued Data Abstract: Discretization of continuously valued data is a useful and necessary tool because many learning paradigms assume nominal data. A list of objectives for efficient and effective discretization is presented. A paradigm called BRACE (Bo...	[ 297, 638 ]	Validation
442	3	Title: Searching for dependencies in Bayesian classifiers j A n V n j If the attributes Abstract: Naive Bayesian classifiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classifier by searching for dependencies among attribut...	[ 401, 635, 1908 ]	Train
443	2	Title: Parameterization studies for the SAM and HMMER methods of hidden Markov model generation Abstract: 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 ...	[ 14 ]	Train
444	2	Title: Fools Gold: Extracting Finite State Machines From Recurrent Network Dynamics Abstract: Several recurrent networks have been proposed as representations for the task of formal language learning. After training a recurrent network, the next step is to understand the information processing carried out by the netw...	[ 144, 753 ]	Train
445	0	Title: Bias and the Probability of Generalization Abstract: In order to be useful, a learning algorithm must be able to generalize well when faced with inputs not previously presented to the system. A bias is necessary for any generalization, and as shown by several researchers in recent years, no bias can lead to st...	[ 318, 690 ]	Validation
446	4	Title: H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward Abstract: In this paper, we introduce a model-based reinforcement learning method called H-learning, which optimizes undiscounted average reward. We compare it with three other reinforcement learning methods in the domain of sc...	[ 552, 554 ]	Train
447	2	Title: A Smooth Converse Lyapunov Theorem for Robust Stability Abstract: This paper presents a Converse Lyapunov Function Theorem motivated by robust control analysis and design. Our result is based upon, but generalizes, various aspects of well-known classical theorems. In a unified and natural manner, it (1) allows...	[ 630, 1471, 1501 ]	Test
448	1	Title: Grounding Robotic Control with Genetic Neural Networks Abstract: Technical Report AI94-223 May 1994 Abstract An important but often neglected problem in the field of Artificial Intelligence is that of grounding systems in their environment such that the representations they manipulate have inherent meaning for...	[ 163, 191 ]	Validation
449	0	Title: Correcting Imperfect Domain Theories: A Knowledge-Level Analysis Abstract: Explanation-Based Learning [Mitchell et al., 1986; DeJong and Mooney, 1986] has shown promise as a powerful analytical learning technique. However, EBL is severely hampered by the requirement of a complete and correct domain theory for ...	[ 479, 566, 638, 1539 ]	Test
450	3	Title: Mapping Bayesian Networks to Boltzmann Machines Abstract: We study the task of tnding a maximal a posteriori (MAP) instantiation of Bayesian network variables, given a partial value assignment as an initial constraint. This problem is known to be NP-hard, so we concentrate on a stochastic approximation algorit...	[ 646, 954, 2558 ]	Validation
451	4	Title: Parameterized Heuristics for Intelligent Adaptive Network Routing in Large Communication Networks Abstract: Parameterized heuristics offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-t...	[ 552, 2537, 2666 ]	Test
452	3	Title: Principal Curve Clustering With Noise Abstract: Technical Report 317 Department of Statistics University of Washington. 1 Derek Stanford is Graduate Research Assistant and Adrian E. Raftery is Professor of Statistics and Sociology, both at the Department of Statistics, University of Washington, Box 354322, Sea...	[ 117, 347, 513 ]	Train
453	6	Title: How to Use Expert Advice (Extended Abstract) Abstract: We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is g...	[ 9, 514, 549, 591, 706, 876, 1025, 1124, 1269, 1358, 1449, 1566, 1661, 1712, 2034, 2059, 2092, 2098, 2099, 2156, 2354, 2455, 2618 ]	Train
454	0	Title: Towards Formalizations in Case-Based Reasoning for Synthesis Abstract: This paper presents the formalization of a novel approach to structural similarity assessment and adaptation in case-based reasoning (Cbr) for synthesis. The approach has been informally presented, exemplified, and implemented for the domai...	[ 183, 539, 541, 1368 ]	Train
455	4	Title: Learning from an Automated Training Agent Abstract: 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 ...	[ 374, 552 ]	Validation
456	6	Title: Boosting a weak learning algorithm by majority To be published in Information and Computation Abstract: We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training th...	[ 25, 549, 569, 672, 1296, 1560, 1748, 2099, 2455, 2653 ]	Train
457	0	Title: A Computational Model of Ratio Decidendi Abstract: This paper proposes a model of ratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This mo...	[ 166, 649 ]	Train
458	2	Title: Quantifying neighbourhood preservation in topographic mappings Abstract: Mappings that preserve neighbourhood relationships are important in many contexts, from neurobiology to multivariate data analysis. It is important to be clear about precisely what is meant by preserving neighbourhoods. At least three iss...	[ 745, 747 ]	Train
459	6	Title: Pac Learning, Noise, and Geometry Abstract: This paper describes the probably approximately correct model of concept learning, paying special attention to the case where instances are points in Euclidean n-space. The problem of learning from noisy training data is also studied.	[ 109, 130, 267, 640, 1705 ]	Train
460	4	Title: Learning Roles: Behavioral Diversity in Robot Teams Abstract: 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 reinforceme...	[ 148, 281 ]	Test
461	2	Title: Product Unit Learning constructive algorithm is then introduced which adds product units to a network Abstract: Product units provide a method of automatically learning the higher-order input combinations required for the efficient synthesis of Boolean logic functions by neural networks. Product units also have ...	[ 427 ]	Train
462	2	Title: Draft Symbolic Representation of Neural Networks Abstract: An early and shorter version of this paper has been accepted for presenta tion at IJCAI'95.	[ 187, 1644, 2582 ]	Test
463	4	Title: A Cognitive Model of Learning to Navigate Abstract: Our goal is to develop a cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper...	[ 3, 333, 477, 483, 564 ]	Validation
464	3	Title: On the Logic of Iterated Belief Revision Abstract: We show in this paper that the AGM postulates are too week to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional ...	[ 275, 279, 467, 573 ]	Validation
465	4	Title: Strategy Learning with Multilayer Connectionist Representations 1 Abstract: Results are presented that demonstrate the learning and fine-tuning of search strategies using connectionist mechanisms. Previous studies of strategy learning within the symbolic, production-rule formalism have not addressed fine-tunin...	[ 85, 103, 118, 294, 466, 523, 565, 566, 2027, 2368, 2672 ]	Train
466	4	Title: On the Computational Economics of Reinforcement Learning Abstract: Following terminology used in adaptive control, we distinguish between indirect learning methods, which learn explicit models of the dynamic structure of the system to be controlled, and direct learning methods, which do not. We compare an exis...	[ 16, 294, 465, 552, 565, 566 ]	Validation
467	3	Title: A Knowledge-Based Framework for Belief Change Part I: Foundations Abstract: We propose a general framework in which to study belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes ' if he knows that ' is true in all the worlds he considers most plausible. We then c...	[ 270, 276, 342, 464, 495, 729, 2000, 2016 ]	Train
468	3	Title: Adaptive Markov Chain Monte Carlo through Regeneration Summary Abstract: Markov chain Monte Carlo (MCMC) is used for evaluating expectations of functions of interest under a target distribution . This is done by calculating averages over the sample path of a Markov chain having as its stationary distribution....	[ 182, 491, 896, 1713, 2377 ]	Train
469	2	Title: Interpolation Models with Multiple Abstract: A traditional interpolation model is characterized by the choice of reg-ularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant ff, and the noise model has a single parameter fi. The ratio f...	[ 78, 160, 214 ]	Test
470	0	Title: What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog Abstract: 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 di...	[ 478 ]	Test
471	4	Title: In Improving Elevator Performance Using Reinforcement Learning Abstract: This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems...	[ 2, 103, 295, 621, 1045, 1632, 1859 ]	Validation
472	4	Title: Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation. Abstract: This paper presents Fringe Exploration, a technique for efficient exploration in partially observable domains. The key idea, (applicable to many exploration techniques), i...	[ 552, 566, 650, 1006 ]	Validation
473	4	Title: Improving Policies without Measuring Merits Abstract: Performing policy iteration in dynamic programming should only require knowledge of relative rather than absolute measures of the utility of actions what Baird (1993) calls the advantages of actions at states. Nevertheless, existing methods in dynamic progr...	[ 552, 1459 ]	Train
474	2	Title: Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools Abstract: We introduce a parallel approach, "DT-Select," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-Select is able to rapidly choose small, nonredundant feature sets ...	[ 635, 698 ]	Train
475	1	Title: Basic PSugal an extension package for the development of Distributed Genetic Algorithms Abstract: This paper presents the extension package developed by the author at the Faculty of Sciences and Technology of the New University of Lisbon, designed for experimentation with Coarse-Grained Distributed Genetic Alg...	[ 168 ]	Train
476	2	Title: A self-organizing multiple-view representation of 3D objects Abstract: We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebb...	[ 605, 1056, 1091 ]	Validation
477	4	Title: Forward models: Supervised learning with a distal teacher Abstract: Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated wit...	[ 229, 294, 333, 427, 463, 565, 566, 745, 1645, 1766, 2409, 2658 ]	Train
478	0	Title: An Improved Algorithm for Incremental Induction of Decision Trees Abstract: Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of...	[ 52, 96, 215, 218, 227, 274, 303, 396, 470, 479, 497, 520, 523, 565, 568, 618, 754 ]	Train
479	0	Title: Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual Abstract: Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract ...	[ 92, 136, 147, 418, 449, 478, 649, 1354, 1627, 2300, 2636 ]	Train
480	2	Title: Modelling the Manifolds of Images of Handwritten Digits Abstract: Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision ...	[ 257, 667, 2270, 2570 ]	Train
481	6	Title: The Weighted Majority Algorithm Abstract: fl This research was primarily conducted while this author was at the University of Calif. at Santa Cruz with support from ONR grant N00014-86-K-0454, and at Harvard University, supported by ONR grant N00014-85-K-0445 and DARPA grant AFOSR-89-0506. Current address: NEC...	[ 9 ]	Train
482	0	Title: Simple Selection of Utile Control Rules in Speedup Learning Abstract: Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented h...	[ 13, 251, 551, 578 ]	Train
483	4	Title: The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces Abstract: Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that learning does not plan uniform...	[ 277, 294, 367, 463, 552, 566, 650, 749, 933 ]	Train
484	3	Title: Comparing Predictive Inference Methods for Discrete Domains Abstract: Predictive inference is seen here as the process of determining the predictive distribution of a discrete variable, given a data set of training examples and the values for the other problem domain variables. We consider three approaches for...	[ 641, 642, 1574 ]	Validation
485	3	Title: Bayesian Case-Based Reasoning with Neural Networks Abstract: Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. In this paper we introduce a neural netw...	[ 711, 1838, 2380, 2514, 2561 ]	Train
486	0	Title: CASE-BASED CREATIVE DESIGN Abstract: Designers across a variety of domains engage in many of the same creative activities. Since much creativity stems from using old solutions in novel ways, we believe that case-based reasoning can be used to explain many creative design processes.	[ 64, 231, 284, 285, 1148, 1278, 1597, 2276 ]	Train
487	2	Title: Language as a dynamical system Abstract: Designers across a variety of domains engage in many of the same creative activities. Since much creativity stems from using old solutions in novel ways, we believe that case-based reasoning can be used to explain many creative design processes.	[ 291, 538 ]	Train
488	6	Title: Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions Abstract: We present a new general-purpose algorithm for learning classes of [0; 1]-valued functions in a generalization of the prediction model, and prove a general upper bound on the expected absolute error of this algorithm in terms o...	[ 109, 549, 591, 2053 ]	Train
489	2	Title: Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1 Abstract: It is widely considered an ultimate connectionist objective to incorporate neural networks into intelligent systems. These systems are intended to possess a varied repertoire of functions enabling adaptable interactio...	[ 46, 238, 301, 747, 1815, 2670 ]	Train
490	4	Title: Learning policies for partially observable environments: Scaling up Abstract: Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to...	[ 5, 6, 45, 213, 220, 492, 734 ]	Validation
491	3	Title: Self Regenerative Markov Chain Monte Carlo Summary Abstract: We propose a new method of construction of Markov chains with a given stationary distribution . This method is based on construction of an auxiliary chain with some other stationary distribution and picking elements of this auxiliary chain a suitabl...	[ 182, 468, 2318, 2377 ]	Train
492	4	Title: Approximating Optimal Policies for Partially Observable Stochastic Domains Abstract: The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligence. If the state of the world is known at all times, the world can be modeled as a Markov Decision Process (MDP). MDPs have bee...	[ 45, 213, 490, 565, 734, 1186, 1741, 2323, 2419 ]	Validation
493	1	Title: Parallel Search for Neural Network Under the guidance of Abstract: The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligence. If the state of the world is known at all times, the world can be modeled as a Markov Decision Process (MDP). MDPs have been studied extensi...	[ 427 ]	Validation

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.