node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
494 | 2 | Title: Connectionist Modeling of the Fast Mapping Phenomenon
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 extensively... | [
427,
747
] | Train |
495 | 3 | Title: Abduction to Plausible Causes: An Event-based Model of Belief Update
Abstract: The Katsuno and Mendelzon (KM) theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We de... | [
270,
339,
342,
467
] | Validation |
496 | 2 | Title: BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN
Abstract: 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 Co... | [
501,
663,
1896,
2393
] | Train |
497 | 6 | Title: Decision Tree Induction Based on Efficient Tree Restructuring
Abstract: The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction... | [
478,
2342
] | Test |
498 | 3 | Title: A variational approach to Bayesian logistic regression models and their extensions
Abstract: We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that accurate variational techniques can be used to obtain a closed form posterior distribution over the parameter... | [
107,
108,
250
] | Train |
499 | 3 | Title: IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS
Abstract: Mean field methods provide computationally efficient approximations to posterior probability distributions for graphical models. Simple mean field methods make a completely factorized approximation to the posterior, which is ... | [
250,
1287,
1288
] | Train |
500 | 4 | Title: 2-D Pole Balancing with Recurrent Evolutionary Networks
Abstract: The success of evolutionary methods on standard control learning tasks has created a need for new benchmarks. The classic pole balancing problem is no longer difficult enough to serve as a viable yardstick for measuring the learning efficiency o... | [
247,
563,
1767,
2444
] | Train |
501 | 2 | Title: Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns
Abstract: This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing... | [
174,
496,
503,
663
] | Train |
502 | 4 | Title: Fast Online Q()
Abstract: 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 ba... | [
294,
565,
567,
747,
2536
] | Train |
503 | 2 | Title: Generative Learning Structures and Processes for Generalized Connectionist Networks
Abstract: Massively parallel networks of relatively simple computing elements offer an attractive and versatile framework for exploring a variety of learning structures and processes for intelligent systems. This paper briefly ... | [
174,
501,
1813,
1851,
1952,
2029,
2396
] | Train |
504 | 2 | Title: MANIAC: A Next Generation Neurally Based Autonomous Road Follower
Abstract: The use of artificial neural networks in the domain of autonomous vehicle navigation has produced promising results. ALVINN [Pomerleau, 1991] has shown that a neural system can drive a vehicle reliably and safely on many different type... | [
702
] | Test |
505 | 2 | Title: From Isolation to Cooperation: An Alternative View of a System of Experts
Abstract: We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for... | [
74,
134
] | Train |
506 | 4 | Title: Dynamic Non-Bayesian Decision Making
Abstract: 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, ... | [
514
] | Train |
507 | 6 | Title: PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples
Abstract: We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q d with respect to product distributions from multiple-instance examples in the PAC model. Here, each example con... | [
109,
549,
798,
1888,
2391,
2427,
2548
] | Validation |
508 | 6 | Title: Machine Learning by Function Decomposition
Abstract: We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less com... | [
317,
417,
523,
2326
] | Test |
509 | 5 | Title: The Bayesian Approach to Tree-Structured Regression
Abstract: In the context of inductive learning, the Bayesian approach turned out to be very successful in estimating probabilities of events when there are only a few learning examples. The m-probability estimate was developed to handle such situations. In th... | [
314,
669
] | Test |
510 | 3 | Title: The Bayesian Approach to Tree-Structured Regression
Abstract: TECHNICAL REPORT NO. 967 August 1996 | [
192,
356,
519,
2223
] | Validation |
511 | 3 | Title: Learning from incomplete data
Abstract: Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives|the likelihood-based and the Bayesian. The goal is two-fol... | [
74,
611
] | Train |
512 | 2 | Title: Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks
Abstract: Recently, we have proven that the dynamics of any deterministic finite-state automata (DFA) with n states and m input symbols can be implemented in a sparse second-order recurrent neural network (SORNN) with n + 1 sta... | [
407,
411
] | Train |
513 | 3 | Title: Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
Abstract: Technical Report No. 295 Department of Statistics, University of Washington October, 1995 1 Abhijit Dasgupta is a graduate student at the Department of Biostatistics, University of Washington, Box 357232, Seattle, W... | [
117,
155,
452
] | Train |
514 | 6 | Title: Gambling in a rigged casino: The adversarial multi-armed bandit problem
Abstract: In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the s... | [
453,
506,
569
] | Validation |
515 | 3 | Title: Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks
Abstract: 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... | [
637,
2164
] | Validation |
516 | 2 | Title: A Supercomputer for Neural Computation
Abstract: The requirement to train large neural networks quickly has prompted the design of a new massively parallel supercomputer using custom VLSI. This design features 128 processing nodes, communicating over a mesh network connected directly to the processor chip. Stu... | [
272
] | Train |
517 | 6 | Title: Active Learning with Committees for Text Categorization
Abstract: In many real-world domains like text categorization, supervised learning requires a large number of training examples. In this paper we describe an active learning method that uses a committee of learners to reduce the number of training example... | [
164,
1170,
1198,
2509
] | Train |
518 | 6 | Title: Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning
Abstract: In this paper I give a review of ensemble learning using a simple example. | [
76,
181,
662,
766,
2532
] | Train |
519 | 2 | Title: Smoothing Spline ANOVA for Exponential Families, with Application to the Wisconsin Epidemiological Study of Diabetic
Abstract: In this paper I give a review of ensemble learning using a simple example. | [
10,
190,
192,
193,
280,
420,
510,
705,
2223,
2448,
2549,
2590,
2608
] | Train |
520 | 2 | Title: CANCER DIAGNOSIS AND PROGNOSIS VIA LINEAR-PROGRAMMING-BASED MACHINE LEARNING
Abstract: In this paper I give a review of ensemble learning using a simple example. | [
142,
230,
478,
524,
719
] | Train |
521 | 5 | Title: Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs
Abstract: covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering works by repeatedly specia... | [
156,
344,
638,
1081,
1082,
1259,
2312
] | Train |
522 | 6 | Title: THE DISCOVERY OF ALGORITHMIC PROBABILITY
Abstract: covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering works by repeatedly specializing an overly general hypothe... | [
68,
525
] | Train |
523 | 1 | Title: Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some
Abstract: covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering wo... | [
54,
163,
188,
277,
283,
415,
465,
478,
508,
540,
551,
565,
717,
870,
882,
910,
961,
1214,
1616,
1676,
1687,
1790,
1921,
1931,
2408,
2442,
2480,
2551,
2600,
2642
] | Validation |
524 | 2 | Title: An Inductive Learning Approach to Prognostic Prediction
Abstract: 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... | [
430,
520,
1169,
1454
] | Train |
525 | 6 | Title: MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions
Abstract: Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wal... | [
522,
684,
1419,
1425,
1427
] | Test |
526 | 2 | Title: MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions
Abstract: 11] M.H. Overmars. A random approach to motion planning. Technical Report RUU-CS-92-32, Department of Computer Science, Utrecht University, October 1992. | [
427,
747
] | Train |
527 | 2 | Title: VISIT: An Efficient Computational Model of Human Visual Attention
Abstract: One of the challenges for models of cognitive phenomena is the development of efficient and exible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an a... | [
747,
1656,
1968,
2606,
2662
] | Train |
528 | 2 | Title: Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks
Abstract: A Lyapunov function for excitatory-inhibitory networks is constructed. The construction assumes symmetric interactions within excitatory and inhibitory populations of neurons, and antisymmetric interactions between populations. The Ly... | [
678
] | Validation |
529 | 2 | Title: Capacity of SDM
Abstract: Report R95:12 ISRN : SICS-R--95/12-SE ISSN : 0283-3638 Abstract A more efficient way of reading the SDM memory is presented. This is accomplished by using implicit information, hitherto not utilized, to find the information-carrying units and thus removing unnecessary noise when readi... | [
340,
341,
709
] | Train |
530 | 1 | Title: operations: operation machine duration
Abstract: Report R95:12 ISRN : SICS-R--95/12-SE ISSN : 0283-3638 Abstract A more efficient way of reading the SDM memory is presented. This is accomplished by using implicit information, hitherto not utilized, to find the information-carrying units and thus removing unnec... | [
163,
343
] | Train |
531 | 2 | Title: FEEDBACK STABILIZATION OF NONLINEAR SYSTEMS
Abstract: This paper surveys some well-known facts as well as some recent developments on the topic of stabilization of nonlinear systems. | [
693,
1490,
2187
] | Validation |
532 | 3 | Title: Hierarchical Selection Models with Applications in Meta-Analysis
Abstract: This paper surveys some well-known facts as well as some recent developments on the topic of stabilization of nonlinear systems. | [
27
] | Train |
533 | 3 | Title: Estimating Ratios of Normalizing Constants for Densities with Different Dimensions
Abstract: In Bayesian inference, a Bayes factor is defined as the ratio of posterior odds versus prior odds where posterior odds is simply a ratio of the normalizing constants of two posterior densities. In many practical proble... | [
41,
777
] | Validation |
534 | 0 | Title: Massively Parallel Matching of Knowledge Structures
Abstract: As knowledge bases used for AI systems increase in size, access to relevant information is the dominant factor in the cost of inference. This is especially true for analogical (or case-based) reasoning, in which the ability of the system to perform ... | [
313
] | Train |
535 | 6 | Title: Sequential PAC Learning
Abstract: We consider the use of "on-line" stopping rules to reduce the number of training examples needed to pac-learn. Rather than collect a large training sample that can be proved sufficient to eliminate all bad hypotheses a priori, the idea is instead to observe training examples o... | [
109,
672,
761,
1560
] | Train |
536 | 2 | Title: Dimension of Recurrent Neural Networks
Abstract: DIMACS Technical Report 96-56 December 1996 | [
58,
200,
206,
411
] | Train |
537 | 1 | Title: Adaptive Global Optimization with Local Search
Abstract: DIMACS Technical Report 96-56 December 1996 | [
357,
606,
1204
] | Test |
538 | 1 | Title: Learning and evolution in neural networks
Abstract: DIMACS Technical Report 96-56 December 1996 | [
15,
129,
402,
487,
1036,
1204,
1689,
1738,
2165,
2193,
2309
] | Train |
539 | 0 | Title: Structural Similarity as Guidance in Case-Based Design
Abstract: This paper presents a novel approach to determine structural similarity as guidance for adaptation in case-based reasoning (Cbr). We advance structural similarity assessment which provides not only a single numeric value but the most specific str... | [
183,
454,
541,
1123,
1209,
1210,
1453,
1665
] | Train |
540 | 0 | Title: A Model-Based Approach to Blame-Assignment in Design
Abstract: We analyze the blame-assignment task in the context of experience-based design and redesign of physical devices. We identify three types of blame-assignment tasks that differ in the types of information they take as input: the design does not achie... | [
523,
543,
603,
1121,
1640
] | Validation |
541 | 0 | Title: Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems
Abstract: We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowledge base of a design support system are defined and illustrated bot... | [
183,
454,
539,
1123
] | Validation |
542 | 2 | Title: Comparison of Bayesian and Neural Net Unsupervised Classification Techniques
Abstract: Unsupervised classification is the classification of data into a number of classes in such a way that data in each class are all similar to each other. In the past there have been few if any studies done to compare the perfo... | [
747,
779,
1203
] | Train |
543 | 0 | Title: Meta-Cases: Explaining Case-Based Reasoning
Abstract: AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important... | [
540
] | Train |
544 | 2 | Title: Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory
Abstract: Statistical decision theory provides a principled way to estimate amino acid frequencies in conserved positions of a protein family. The goal is to minimize the risk function, or the expected squared-error distance between... | [
0,
14,
258,
751
] | Test |
545 | 2 | Title: Characterising Innateness in Artificial and Natural Learning
Abstract: The purpose of this paper is to propose a refinement of the notion of innateness. If we merely identify innateness with bias, then we obtain a poor characterisation of this notion, since any learning device relies on a bias that makes it ch... | [
747
] | Validation |
546 | 1 | Title: GREQE a Diplome des Etudes Approfondies en Economie Mathematique et Econometrie A Genetic Algorithm for
Abstract: The purpose of this paper is to propose a refinement of the notion of innateness. If we merely identify innateness with bias, then we obtain a poor characterisation of this notion, since any learning... | [
163
] | Train |
547 | 2 | Title: Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle
Abstract: Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN [Pomerl... | [
74,
430
] | Train |
548 | 4 | Title: Value Function Based Production Scheduling
Abstract: Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of maintaining product inventories in the face of unpredictable demand and stoch... | [
82,
552,
565,
1859,
1860
] | Train |
549 | 6 | Title: Efficient Distribution-free Learning of Probabilistic Concepts
Abstract: In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior|thus, the same input may sometimes be classified as a positive examp... | [
287,
453,
456,
488,
507,
574,
591,
640,
672
] | Train |
550 | 6 | Title: LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION
Abstract: | [
438,
569,
1422,
2423
] | Test |
551 | 4 | Title: Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of
Abstract: This paper highlights a phenomenon that causes deductively learned knowledge to be harmful when used for problem solving. The problem occurs when deductive problem solvers encounter a failure ... | [
482,
523,
2215,
2473
] | Train |
552 | 4 | Title: Learning to Act using Real-Time Dynamic Programming
Abstract: fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their... | [
2,
16,
57,
60,
85,
92,
167,
173,
210,
220,
239,
277,
298,
305,
306,
311,
367,
370,
374,
412,
446,
451,
455,
466,
472,
473,
483,
548,
554,
559,
575,
601,
621,
636,
644,
653,
671,
688,
691,
723,
738,
749
] | Train |
553 | 2 | Title: Object Selection Based on Oscillatory Correlation
Abstract: 1 Technical Report: OSU-CISRC-12/96 - TR67, 1996 Abstract One of the classical topics in neural networks is winner-take-all (WTA), which has been widely used in unsupervised (competitive) learning, cortical processing, and attentional control. Because... | [
123,
2459
] | Train |
554 | 4 | Title: Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes
Abstract: Reinforcement learning (RL) has become a central paradigm for solving learning-control problems in robotics and artificial intelligence. RL researchers have focussed almost exclusively on problems where the controller h... | [
167,
294,
306,
446,
552,
565,
875,
1859
] | Train |
555 | 6 | Title: Exactly Learning Automata with Small Cover Time
Abstract: We present algorithms for exactly learning unknown environments that can be described by deterministic finite automata. The learner performs a walk on the target automaton, where at each step it observes the output of the state it is at, and chooses a l... | [
400,
556,
615,
2354
] | Test |
556 | 6 | Title: The Power of a Pebble: Exploring and Mapping Directed Graphs
Abstract: Exploring and mapping an unknown environment is a fundamental problem, which is studied in a variety of contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a mod... | [
555,
615,
2354,
2360
] | Test |
557 | 3 | Title: On the Sample Complexity of Learning Bayesian Networks
Abstract: In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that t... | [
423,
558
] | Train |
558 | 3 | Title: A Tutorial on Learning With Bayesian Networks
Abstract: Technical Report MSR-TR-95-06 | [
376,
423,
557,
905,
1137,
1532,
1555,
1641,
1816,
1934,
2034,
2463,
2660
] | Train |
559 | 4 | Title: Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function
Abstract: Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose tw... | [
34,
167,
552,
1378,
1816,
2341
] | Train |
560 | 6 | Title: Bayesian Methods for Adaptive Models
Abstract: Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address ... | [
78,
157,
246,
740,
938,
955,
1375,
1452,
2287
] | Test |
561 | 2 | Title: Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Abstract: Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a m... | [
427,
745,
747
] | Train |
562 | 4 | Title: Transfer of Learning by Composing Solutions of Elemental Sequential Tasks
Abstract: Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I co... | [
60,
252,
370,
440,
671,
1117,
1183,
1401,
1889,
2014,
2018
] | Train |
563 | 4 | Title: Evolving Obstacle Avoidance Behavior in a Robot Arm
Abstract: Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior a... | [
37,
38,
163,
219,
247,
500
] | Train |
564 | 4 | Title: Reinforcement Learning with Soft State Aggregation
Abstract: It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling reinforcement learning (RL) algorithms to real-world problems. Unfortunately almost all of the theory of reinforcement learning assumes lookup... | [
294,
463,
565,
738,
1841
] | Train |
565 | 4 | Title: Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,
Abstract: This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its futur... | [
2,
34,
57,
60,
82,
85,
92,
103,
118,
128,
173,
239,
244,
283,
294,
295,
305,
306,
333,
367,
385,
410,
425,
465,
466,
477,
478,
492,
502,
523,
548,
554,
564,
566,
575,
601,
621,
633,
644,
671,
691,
738,
773,
842,
882,
910,
10... | Train |
566 | 4 | Title: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
Abstract: This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinf... | [
16,
34,
173,
186,
274,
294,
321,
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472,
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588,
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688,
699,
733,
858,
1447,
1459,
1544,
1643,
1782,
1816,
2221,
2480,
2485,
2658
] | Validation |
567 | 4 | Title: Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding
Abstract: 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 s... | [
21,
277,
385,
502,
970,
1828
] | Train |
568 | 4 | Title: Online Learning with Random Representations
Abstract: We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are... | [
478,
843
] | Train |
569 | 6 | Title: A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule
Abstract: We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension ... | [
255,
456,
514,
550,
710,
767,
1000,
1025,
1092,
1181,
1269,
1273,
1430,
1457,
1522,
1712,
1986,
2099
] | Train |
570 | 2 | Title: A New Learning Algorithm for Blind Signal Separation
Abstract: A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the m... | [
59,
169,
212,
576,
872,
874,
1067,
1200,
1243,
1245,
1246,
1258,
1381,
1520,
1524,
1709,
1814,
1922,
2026
] | Train |
571 | 6 | Title: The Central Classifier Bound ANew Error Bound for the Classifier Chosen by Early Stopping Key
Abstract: A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of th... | [
19,
424,
1762,
2331,
2495,
2694
] | Validation |
572 | 2 | Title: Avoiding Overfitting with BP-SOM
Abstract: 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, ... | [
112,
624,
747,
881
] | Train |
573 | 3 | Title: Iterated Revision and Minimal Change of Conditional Beliefs
Abstract: We describe a model of iterated belief revision that extends the AGM theory of revision to account for the effect of a revision on the conditional beliefs of an agent. In particular, this model ensures that an agent makes as few changes as p... | [
270,
464
] | Test |
574 | 6 | Title: On the Learnability of Discrete Distributions (extended abstract)
Abstract: We describe a model of iterated belief revision that extends the AGM theory of revision to account for the effect of a revision on the conditional beliefs of an agent. In particular, this model ensures that an agent makes as few change... | [
242,
549,
640,
672,
1006,
1827,
1962,
2040,
2360,
2475
] | Train |
575 | 4 | Title: Issues in Using Function Approximation for Reinforcement Learning
Abstract: 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 wi... | [
173,
552,
565,
738,
843,
882,
1378,
2485
] | Train |
576 | 2 | Title: An information-maximisation approach to blind separation and blind deconvolution
Abstract: We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here... | [
43,
59,
212,
293,
330,
354,
355,
570,
605,
726,
731,
834,
839,
863,
874,
1014,
1067,
1200,
1245,
1258,
1381,
1524,
1526,
1710,
1801,
1814,
1922,
1932,
2026,
2552
] | Train |
577 | 3 | Title: Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide
Abstract: This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, d... | [
250,
312,
389,
401,
1502,
1532,
2034,
2492,
2660
] | Test |
578 | 0 | Title: AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING
Abstract: The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (co... | [
13,
482,
1122,
1333,
1877
] | Train |
579 | 2 | Title: Comparison of Kernel Estimators, Perceptrons, and Radial-Basis Functions for OCR and Speech Classification
Abstract: We compare kernel estimators, single and multi-layered perceptrons and radial-basis functions for the problems of classification of handwritten digits and speech phonemes. By taking two differen... | [
611,
696,
747
] | Train |
580 | 0 | Title: Learning to Improve Case Adaptation by Introspective Reasoning and CBR
Abstract: In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that... | [
581,
922,
1126,
1212,
1215,
1497
] | Train |
581 | 0 | Title: Representing Self-knowledge for Introspection about Memory Search
Abstract: This position paper sketches a framework for modeling introspective reasoning and discusses the relevance of that framework for modeling introspective reasoning about memory search. It argues that effective and flexible memory processi... | [
49,
50,
222,
580
] | Train |
582 | 0 | Title: In Machine Learning: A Multistrategy Approach, Vol. IV Macro and Micro Perspectives of Multistrategy Learning
Abstract: Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most mach... | [
259,
818,
1498,
1792
] | Train |
583 | 0 | Title: Introspective reasoning using meta-explanations for multistrategy learning
Abstract: In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pi... | [
50,
64,
284,
643,
1126,
1214,
1278,
2371,
2398,
2568
] | Train |
584 | 3 | Title: A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS
Abstract: We introduce a learning algorithm for unsupervised neural networks based on ideas from statistical mechanics. The algorithm is derived from a mean field approximation for large, layered sigmoid belief networks. We show how to (approxima... | [
250,
427,
639
] | Test |
585 | 5 | Title: An investigation of noise-tolerant relational concept learning algorithms
Abstract: We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally. | [
335,
378,
911,
1061,
1275,
2091,
2290,
2291
] | Validation |
586 | 2 | Title: Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify
Abstract: Technical Report UMIACS-TR-97-77 and CS-TR-3843 Abstract | [
28
] | Test |
587 | 2 | Title: NONPARAMETRIC SELECTION OF INPUT VARIABLES FOR CONNECTIONIST LEARNING
Abstract: Technical Report UMIACS-TR-97-77 and CS-TR-3843 Abstract | [
88,
214,
427,
2239
] | Train |
588 | 4 | Title: LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION
Abstract: The paper describes a self-learning control system for a mobile robot. Based on sensor information the control system has to provide a steering signal in such a way that collisions are avoided. Since in our ca... | [
186,
294,
566,
699,
747
] | Train |
589 | 2 | Title: BRIGHTNESS PERCEPTION, ILLUSORY CONTOURS, AND CORTICOGENICULATE FEEDBACK
Abstract: fl Partially supported by the Advanced Research Projects Agency (AFOSR 90-0083). y Partially supported by the Air Force Office of Scientific Research (AFOSR F49620-92-J-0499), the Advanced Research Projects Agency (ONR N00014-92... | [
282,
592,
1509
] | Train |
590 | 2 | Title: APPROXIMATION IN L p (R d FROM SPACES SPANNED BY THE PERTURBED INTEGER TRANSLATES OF
Abstract: May 14, 1995 Abstract. The problem of approximating smooth L p -functions from spaces spanned by the integer translates of a radially symmetric function is very well understood. In case the points of translation, ffi, ... | [
364,
365,
366
] | Train |
591 | 6 | Title: Toward Efficient Agnostic Learning
Abstract: In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning,... | [
199,
287,
453,
488,
549,
640,
672,
848,
1032,
1105,
1186,
1358,
2054,
2155,
2182,
2475,
2690
] | Test |
592 | 2 | Title: FIGURE-GROUND SEPARATION BY VISUAL CORTEX Encyclopedia of Neuroscience
Abstract: In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is... | [
282,
589,
1144,
1509
] | Validation |
593 | 0 | Title: THE DESIGN AND IMPLEMENTATION OF A CASE-BASED PLANNING FRAMEWORK WITHIN A PARTIAL-ORDER PLANNER
Abstract: In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate g... | [
300,
594
] | Train |
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