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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
294	4	Title: References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towar...	[ 21, 83, 85, 103, 128, 153, 173, 186, 191, 252, 277, 465, 466, 477, 483, 502, 554, 564, 565, 566, 588, 633, 636, 638, 665, 699, 807, 1353, 1438, 1672, 2430, 2476 ]	Train
295	4	Title: A Neuro-Dynamic Programming Approach to Retailer Inventory Management 1 Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic n...	[ 82, 197, 210, 220, 471, 565, 1012, 1316, 1782, 2078 ]	Train
296	6	Title: Lookahead and Pathology in Decision Tree Induction Abstract: The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-le...	[ 227, 438, 638, 692, 1236 ]	Train
297	2	Title: Automatic Feature Extraction in Machine Learning Abstract: This thesis presents a machine learning model capable of extracting discrete classes out of continuous valued input features. This is done using a neurally inspired novel competitive classifier (CC) which feeds the discrete classifications forward to a...	[ 441, 809, 1321 ]	Test
298	4	Title: How to Dynamically Merge Markov Decision Processes Abstract: 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...	[ 410, 552 ]	Train
299	6	Title: On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics) Abstract: We consider the problem of fitting an n fi n distance matrix D by a tree metric T . Let " be the distance to the closest tree metric, that is, " = min T fk T; D k 1 g. First we present an O(n 2 ) algorithm for finding an...	[ 596, 746, 1827, 2083, 2110 ]	Train
300	0	Title: Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures Abstract: Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval a...	[ 593, 1621 ]	Train
301	2	Title: Data Exploration with Reflective Adaptive Models Abstract: Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences...	[ 46, 238, 489 ]	Validation
302	5	Title: Confidence Estimation for Speculation Control Abstract: Modern processors improve instruction level parallelism by speculation. The outcome of data and control decisions is predicted, and the operations are speculatively executed and only committed if the original predictions were correct. There are a number o...	[ 428, 432, 598 ]	Train
303	0	Title: Relating Relational Learning Algorithms Abstract: Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being expl...	[ 426, 478, 1174, 2091 ]	Train
304	2	Title: Boltzmann Machine learning using mean field theory and linear response correction Abstract: We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the correlations is given b...	[ 108, 250, 427, 1461, 1912 ]	Validation
305	4	Title: Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling Abstract: This paper introduces a methodology for solving combinatorial optimization problems through the application of reinforcement learning methods. The approach can be appli...	[ 82, 410, 552, 565 ]	Train
306	4	Title: Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards Abstract: Markov decision processes (MDPs) with undis-counted rewards represent an important class of problems in decision and control. The goal of learning in these MDPs is to find a policy that yields the maximum expected return pe...	[ 57, 552, 554, 565, 967, 1376 ]	Train
307	5	Title: A Comparison of Full and Partial Predicated Execution Support for ILP Processors Abstract: One can effectively utilize predicated execution to improve branch handling in instruction-level parallel processors. Although the potential benefits of predicated execution are high, the tradeoffs involved in the design...	[ 598, 735 ]	Test
308	6	Title: The Power of Self-Directed Learning Abstract: This paper studies self-directed learning, a variant of the on-line learning model in which the learner selects the presentation order for the instances. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, k-term D...	[ 9, 1456, 2028 ]	Train
309	1	Title: The Power of Self-Directed Learning Abstract: A lower-bound result on the power of Abstract This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that ...	[ 163 ]	Train
310	2	Title: Forecasting electricity demand using nonlinear mixture of experts Abstract: In this paper we study a forecasting model based on mixture of experts for predicting the French electric daily consumption energy. We split the task into two parts. Using mixture of experts, a first model predicts the electricity dema...	[ 74, 668, 747, 2513 ]	Train
311	2	Title: June 1994 T o app ear in Neural Computation A Coun terexample to T emp Abstract: Sutton's TD( ) metho d aims to provide a represen tation of the cost function in an absorbing Mark ov chain with transition costs. A simple example is given where the represen tation obtained dep ends on . For = 1 the represen tatio...	[ 406, 552 ]	Train
312	3	Title: Chain graphs for learning Abstract:	[ 427, 577, 645, 772 ]	Train
313	0	Title: The Case for Graph-Structured Representations Abstract: Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations bet...	[ 534, 801, 1354, 1377, 1642 ]	Validation
314	5	Title: Employing Linear Regression in Regression Tree Leaves Abstract: The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on ...	[ 156, 509, 1073, 1244, 1596, 1684, 1726 ]	Train
315	2	Title: Cortical Functionality Emergence: Self-Organization of Complex Structures: From Individual to Collective Dynamics, Abstract: General Theory & Quantitative Results Abstract: The human genotype represents at most ten billion binary informations, whereas the human brain contains more than a million times a billi...	[ 747 ]	Train
316	5	Title: Cortical Functionality Emergence: Self-Organization of Complex Structures: From Individual to Collective Dynamics, Abstract: A Methodology for Evaluating Theory Revision Systems: Results Abstract Theory revision systems are learning systems that have a goal of making small changes to an original theory to acc...	[ 344 ]	Validation
317	6	Title: A dataset decomposition approach to data mining and machine discovery Abstract: We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is exp...	[ 417, 508, 2326 ]	Train
318	0	Title: Generalizing from Case Studies: A Case Study Abstract: Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that o...	[ 426, 445, 686, 991, 1173, 1644, 2310, 2333, 2427 ]	Train
319	6	Title: Error Reduction through Learning Multiple Descriptions Abstract: Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understan...	[ 29, 1273 ]	Train
320	2	Title: Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Abstract: This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar imag...	[ 259 ]	Train
321	4	Title: Planning with Closed-Loop Macro Actions Abstract: Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach to this problem based on the mathematical framework of Markov decision processes and reinforcement learning. Co...	[ 566, 1192, 1954, 2179, 2305 ]	Test
322	6	Title: Statistical Tests for Comparing Supervised Classification Learning Algorithms Abstract: This paper reviews five statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrect...	[ 15, 160, 967, 1027, 1644, 2508 ]	Train
323	6	Title: Learning Active Classifiers Abstract: Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can \| at some cost \| obtain the values of missing attributes, bef...	[ 140, 228, 1322, 2467, 2560 ]	Test
324	3	Title: BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE Abstract: Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emph...	[ 62, 185, 278, 326, 327, 332, 389 ]	Test
325	3	Title: Bayesian Model Selection in Social Research (with Discussion by Abstract: 1 This article will be published in Sociological Methodology 1995, edited by Peter V. Marsden, Cambridge, Mass.: Blackwells. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Sociology, DK-40, University of Washin...	[ 211, 1201 ]	Test
326	3	Title: Topological Parameters for time-space tradeoff Abstract: In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. B...	[ 278, 324, 327 ]	Train
327	3	Title: Global Conditioning for Probabilistic Inference in Belief Networks Abstract: In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that global conditioning, as w...	[ 324, 326, 945, 1532, 1899 ]	Test
328	2	Title: Associative memory using action potential timing Abstract: The dynamics and collective properties of feedback networks with spiking neurons are investigated. Special emphasis is given to the potential computational role of subthreshold oscillations. It is shown that model systems with integrate-and-fire neuron...	[ 747, 2619 ]	Validation
329	1	Title: Simple Subpopulation Schemes Abstract: This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which a...	[ 237 ]	Test
330	2	Title: Local Feature Analysis: A general statistical theory for object representation Abstract: This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with t...	[ 354, 359, 576, 731, 747 ]	Train
331	2	Title: From Data Distributions to Regularization in Invariant Learning Abstract: Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing the training data to include examples of inputs transform...	[ 101, 179, 774 ]	Validation
332	3	Title: Exploiting Causal Independence in Bayesian Network Inference Abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional proba...	[ 62, 185, 324, 389, 1064 ]	Train
333	4	Title: A Comparison of Action Selection Learning Methods Abstract: Our goal is to develop a hybrid cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing hybrid computational architectures for the NRL Navigation task, which requires competent senso-rimotor coor...	[ 463, 477, 565, 566 ]	Train
334	6	Title: Improved Noise-Tolerant Learning and Generalized Statistical Queries Abstract: The statistical query learning model can be viewed as a tool for creating (or demonstrating the existence of) noise-tolerant learning algorithms in the PAC model. The complexity of a statistical query algorithm, in conjunction with ...	[ 20, 267 ]	Test
335	5	Title: Incremental Reduced Error Pruning Abstract: This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments...	[ 344, 378, 426, 585 ]	Train
336	2	Title: PREENS Tutorial How to use tools and NN simulations Abstract: 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.	[ 747 ]	Train
337	2	Title: Meter as Mechanism: A Neural Network that Learns Metrical Patterns Abstract: One kind of prosodic structure that apparently underlies both music and language is meter. Yet detailed measurements of both music and speech show that the nested periodicities that define metrical structure are noisy in some sense. W...	[ 77, 143, 346, 363 ]	Validation
338	2	Title: Knowledge Integration and Rule Extraction in Neural Networks Ph.D. Proposal Abstract: One kind of prosodic structure that apparently underlies both music and language is meter. Yet detailed measurements of both music and speech show that the nested periodicities that define metrical structure are noisy in some...	[ 627 ]	Validation
339	3	Title: Abduction as Belief Revision Abstract: We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adeq...	[ 270, 342, 495, 1549, 1602 ]	Train
340	2	Title: Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory Abstract: Report R95:09 ISRN : SICS-R-95/09-SE ISSN : 0283-3638 Abstract The optimal probability of activation and the corresponding performance is studied for three designs of Sparse Distributed Memory,...	[ 341, 529, 709 ]	Train
341	2	Title: Some Comments on the Information Stored in Sparse Distributed Memory Abstract: Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to ...	[ 340, 529, 709 ]	Train
342	3	Title: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are a...	[ 270, 276, 339, 467, 495, 729, 776, 1800, 1945, 1993, 2016, 2546 ]	Train
343	1	Title: A Promising genetic Algorithm Approach to Job-Shop Scheduling, Rescheduling, and Open-Shop Scheduling Problems Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are autom...	[ 530, 1098, 1274, 1303, 1523, 1571, 1577 ]	Test
344	5	Title: Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities...	[ 1, 316, 335, 348, 521, 675, 963, 1007, 1244, 1260, 1267, 1275, 1312, 1442, 1445, 1622, 1627, 1671, 1881, 2032, 2171, 2215, 2229, 2290, 2291, 2339, 2426, 2441, 2589, 2609, 2617 ]	Train
345	3	Title: On Convergence Properties of the EM Algorithm for Gaussian Mixtures Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is o...	[ 74, 76, 117, 261, 622, 1924, 2266, 2389, 2421 ]	Train
346	2	Title: PERCEPTION OF TIME AS PHASE: TOWARD AN ADAPTIVE-OSCILLATOR MODEL OF RHYTHMIC PATTERN PROCESSING 1 Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the E...	[ 132, 143, 163, 337, 363 ]	Train
347	3	Title: A Reference Bayesian Test for Nested Hypotheses And its Relationship to the Schwarz Criterion Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM st...	[ 84, 452, 713, 998, 999 ]	Train
348	5	Title: First Order Regression: Applications in Real-World Domains Abstract: A first order regression algorithm capable of handling real-valued (continuous) variables is introduced and some of its applications are presented. Regressional learning assumes real-valued class and discrete or real-valued variables. The alg...	[ 344, 638, 1244, 1596 ]	Test
349	2	Title: Measuring Organization and Asymmetry in Bihemispheric Topographic Maps Abstract: We address the problem of measuring the degree of hemispheric organization and asymmetry of organization in a computational model of a bihemispheric cerebral cortex. A theoretical framework for such measures is developed and used ...	[ 747 ]	Validation
350	2	Title: Induction of Multiscale Temporal Structure Abstract: Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequ...	[ 111, 180, 201, 351, 427, 664, 730, 770 ]	Test
351	2	Title: Sequence Learning with Incremental Higher-Order Neural Networks Abstract: An incremental, higher-order, non-recurrent neural-network combines two properties found to be useful for sequence learning in neural-networks: higher-order connections and the incremental introduction of new units. The incremental, high...	[ 350, 730 ]	Train
352	3	Title: Convergence controls for MCMC algorithms, with applications to hidden Markov chains Abstract: In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust m...	[ 41, 904, 1372 ]	Train
353	2	Title: Application of Neural Networks for the Classification of Diffuse Liver Disease by Quantitative Echography Abstract: Three different methods were investigated to determine their ability to detect and classify various categories of diffuse liver disease. A statistical method, i.e., discriminant analysis, a super...	[ 427, 747 ]	Train
354	2	Title: Principal and Independent Components in Neural Networks Recent Developments Abstract: Nonlinear extensions of one-unit and multi-unit Principal Component Analysis (PCA) neural networks, introduced earlier by the authors, are reviewed. The networks and their nonlinear Hebbian learning rules are related to other...	[ 330, 576, 839, 1072, 1520 ]	Train
355	2	Title: Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning Abstract: Acknowledgements: This research was supported in part by the Office of Naval Research (Cognitive and Neural Sciences, N00014-93-1-0208) and by the Whitaker Foundation (Special Opportunity Grant). We thank George Kalar...	[ 576, 745, 1093, 1094, 1562, 2068 ]	Train
356	2	Title: A Flexible Model For Human Circadian Rhythms Abstract: Many hormones and other physiological processes vary in a circadian pattern. Although a sine/cosine function can be used to model these patterns, this functional form is not appropriate when there is asymmetry between the peak and nadir phases. In this pap...	[ 190, 510 ]	Validation
357	1	Title: Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization Abstract: We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. ...	[ 243, 537, 803, 1563, 2089 ]	Train
358	3	Title: Hierarchical Spatio-Temporal Mapping of Disease Rates Abstract: Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with socio-demographic census information, they also permit assessment of environmental justice, i.e., whether certain subgroups s...	[ 95, 1255 ]	Train
359	2	Title: Feature Extraction Using an Unsupervised Neural Network Abstract: A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight int...	[ 203, 330, 808, 1068, 1320, 1342, 1787, 1871, 2322, 2376, 2422, 2498, 2499, 2505, 2567 ]	Train
360	2	Title: Investigating the Value of a Good Input Representation Abstract: This paper is reprinted from Computational Learning Theory and Natural Learning Systems, vol. 3, T. Petsche, S. Judd, and S. Hanson, (eds.), forthcoming 1995. Copyrighted 1995 by MIT Press Abstract The ability of an inductive learning system to f...	[ 151, 698 ]	Train
361	1	Title: Overview of Selection Schemes and a Suggested Classification Abstract: In this paper we emphasize the role of selection in evolutionary algorithms. We briefly review some of the most common selection schemes from the fields of Genetic Algorithms, Evolution Strategies and Genetic Programming. However we do not ...	[ 55 ]	Train
362	2	Title: Learning Topology-Preserving Maps Using Self-Supervised Backpropagation Abstract: Self-supervised backpropagation is an unsupervised learning procedure for feedforward networks, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators runni...	[ 747, 2191 ]	Train
363	2	Title: Representing Rhythmic Patterns in a Network of Oscillators Abstract: This paper describes an evolving computational model of the perception and production of simple rhythmic patterns. The model consists of a network of oscillators of different resting frequencies which couple with input patterns and with each ...	[ 143, 337, 346 ]	Validation
364	2	Title: Radial Basis Functions: L p -approximation orders with scattered centres Abstract: In this paper we generalize several results on uniform approximation orders with radial basis functions in (Buhmann, Dyn and Levin, 1993) and (Dyn and Ron, 1993) to L p -approximation orders. These results apply, in particular, ...	[ 365, 366, 590 ]	Train
365	2	Title: Radial basis function approximation: from gridded centers to scattered centers Abstract: The paper studies L 1 (IR d )-norm approximations from a space spanned by a discrete set of translates of a basis function . Attention here is restricted to functions whose Fourier transform is smooth on IR d n0, and has a...	[ 364, 590, 2112, 2572 ]	Test
366	2	Title: AN UPPER BOUND ON THE APPROXIMATION POWER OF PRINCIPAL SHIFT-INVARIANT SPACES Abstract: An upper bound on the L p -approximation power (1 p 1) provided by principal shift-invariant spaces is derived with only very mild assumptions on the generator. It applies to both stationary and non-stationary ladders, and ...	[ 364, 590 ]	Validation
367	4	Title: Machine Learning, Explanation-Based Learning and Reinforcement Learning: A Unified View Abstract: In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods invol...	[ 440, 483, 552, 565 ]	Train
368	2	Title: Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification Abstract: In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing st...	[ 611, 747 ]	Test
369	2	Title: Limitations of self-organizing maps for vector quantization and multidimensional scaling Abstract: The limitations of using self-organizing maps (SOM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant the...	[ 747 ]	Train
370	4	Title: Robust Reinforcement Learning in Motion Planning Abstract: While exploring to find better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, or even catastrophic, results, often modeled in terms of reaching `fail...	[ 552, 562, 875 ]	Validation
371	3	Title: Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation Abstract: In learning problems where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source o...	[ 88, 157, 2507 ]	Validation
372	1	Title: Investigating the role of diploidy in simulated populations of evolving individuals Abstract: In most work applying genetic algorithms to populations of neural networks there is no real distinction between genotype and phenotype. In nature both the information contained in the genotype and the mapping of the g...	[ 38, 273 ]	Train
373	5	Title: Task Selection for a Multiscalar Processor Abstract: 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 ...	[ 86, 652 ]	Test
374	4	Title: An Introspection Approach to Querying a Trainer Abstract: Technical Report 96-13 January 22, 1996 Abstract This paper introduces the Introspection Approach, a method by which a learning agent employing reinforcement learning can decide when to ask a training agent for instruction. When using our approach, we f...	[ 455, 552 ]	Validation
375	6	Title: Constructive Induction Using a Non-Greedy Strategy for Feature Selection Abstract: We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperf...	[ 635, 638, 836, 1576 ]	Test
376	3	Title: Bayesian Finite Mixtures for Nonlinear Modeling of Educational data Abstract: In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full join...	[ 558, 641, 704 ]	Train
377	2	Title: Constructive Algorithms for Hierarchical Mixtures of Experts Abstract: 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 adaptivel...	[ 74, 622 ]	Train
378	6	Title: Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine Abstract: Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of T...	[ 178, 218, 227, 286, 335, 396, 585, 692, 960, 1027, 1061, 1207, 1238, 1275, 1290, 1539, 1644, 1678, 1963, 2012, 2042, 2195, 2290, 2291, 2447, 2583 ]	Train
379	5	Title: In: Machine Learning, Meta-reasoning and Logics, pp207-232, Learning from Imperfect Data Abstract: Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data a...	[ 176, 756 ]	Train
380	1	Title: Fitness Landscapes and Difficulty in Genetic Programming Abstract: The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty o...	[ 163, 188, 934, 1257, 1473, 1474, 1737, 1784, 2196, 2641 ]	Validation
381	6	Title: Compression-Based Feature Subset Selection Keywords: Minimum Description Length Principle, Cross Validation, Noise Abstract: Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of r...	[ 430, 635, 686, 2342 ]	Train
382	6	Title: Learning Decision Lists Using Homogeneous Rules Abstract: A decision list is an ordered list of conjunctive rules (?). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem \| the classification accuracy of the decision list...	[ 29, 1236, 1837, 2132 ]	Validation
383	2	Title: Constructing Fuzzy Graphs from Examples Abstract: Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated ...	[ 87, 631, 638 ]	Train
384	2	Title: Hidden Markov Model Analysis of Motifs in Steroid Dehydrogenases and their Homologs Abstract: Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing ...	[ 14 ]	Train
385	4	Title: Modeling the Student with Reinforcement Learning Abstract: We describe a methodology for enabling an intelligent teaching system to make high level strategy decisions on the basis of low level student modeling information. This framework is less costly to construct, and superior to hand coding teaching strateg...	[ 565, 567 ]	Train
386	2	Title: Temporal Compositional Processing by a DSOM Hierarchical Model Abstract: Any intelligent system, whether human or robotic, must be capable of dealing with patterns over time. Temporal pattern processing can be achieved if the system has a short-term memory capacity (STM) so that different representations can b...	[ 611, 745, 747 ]	Train
387	2	Title: JUNG ET AL.: ESTIMATING ALERTNESS FORM THE EEG POWER SPECTRUM 1 Estimating Alertness from the Abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. ...	[ 293 ]	Test
388	2	Title: Spatial-Temporal Analysis of Temperature Using Smoothing Spline ANOVA Abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electro...	[ 439, 2590 ]	Test
389	3	Title: Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions Abstract: This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calcula...	[ 185, 324, 332, 577, 1937 ]	Validation
390	1	Title: Chaos, Fractals, and Genetic Algorithms Abstract: This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations...	[ 145, 163 ]	Train
391	2	Title: Geometry in Learning Abstract: One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some fu...	[ 142, 230, 427, 438, 1283 ]	Train
392	3	Title: DRAFT Cluster-Weighted Modeling for Time Series Prediction and Characterization Abstract:	[ 76, 154 ]	Train
393	2	Title: Density Networks and their Application to Protein Modelling Abstract: I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probabili...	[ 14, 157 ]	Train

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