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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|---|---|---|---|---|
2,394 | 2 | Title: A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction
Abstract: In drug activity prediction (as in handwritten character recognition), the features extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In handwritten character rec... | [
2427
] | Train |
2,395 | 0 | Title: Improving Competence by Integrating Case-Based Reasoning and Heuristic Search
Abstract: We analyse the behaviour of a Propose & Revise architecture in the VT elevator design problem and we show that this problem solving method cannot solve all possible cases covered by the available domain knowledge. We invest... | [
2665
] | Train |
2,396 | 1 | Title: Properties of Genetic Representations of Neural Architectures
Abstract: Genetic algorithms and related evolutionary techniques offer a promising approach for automatically exploring the design space of neural architectures for artificial intelligence and cognitive modeling. Central to this process of evolution... | [
163,
503,
1583,
1952,
2563
] | Test |
2,397 | 2 | Title: CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial
Abstract: and load balancing even for irregular neural networks. The idea to achieve these goals lies in the programming model: CuPit-2 programs are object-centered, with connections and nodes of a graph (which is the neural net... | [
2203,
2405
] | Train |
2,398 | 0 | Title: Issues in Goal-Driven Explanation
Abstract: When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reason... | [
583,
1498,
2184,
2372,
2399
] | Test |
2,399 | 0 | Title: Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation*
Abstract: When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build e... | [
136,
2398,
2626
] | Train |
2,400 | 2 | Title: A Neural Network Model of Visual Tilt Aftereffects
Abstract: RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the lon... | [
122,
124,
127,
1916
] | Train |
2,401 | 3 | Title: Factor Graphs and Algorithms
Abstract: A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a ... | [
1988
] | Test |
2,402 | 1 | Title: Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming
Abstract: | [
1921,
1931
] | Train |
2,403 | 0 | Title: Reasoning with Portions of Precedents
Abstract: This paper argues that the task of matching in case-based reasoning can often be improved by comparing new cases to portions of precedents. An example is presented that illustrates how combining portions of multiple precedents can permit new cases to be resolved ... | [
649,
2581
] | Train |
2,404 | 3 | Title: A Model for Projection and Action
Abstract: In designing autonomous agents that deal competently with issues involving time and space, there is a tradeoff to be made between guaranteed response-time reactions on the one hand, and flexibility and expressiveness on the other. We propose a model of action with pr... | [
1459,
2221
] | Train |
2,405 | 2 | Title: A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to
Abstract: A compiler for CuPit has been built for the MasPar MP-1/MP-2 using compilation techniques that can also be applied to most other parallel machines. The paper shortly presents the main ideas of the tech... | [
881,
1119,
2203,
2397
] | Validation |
2,406 | 4 | Title: Approximating Value Trees in Structured Dynamic Programming
Abstract: We propose and examine a method of approximate dynamic programming for Markov decision processes based on structured problem representations. We assume an MDP is represented using a dynamic Bayesian network, and construct value functions usi... | [
2078
] | Validation |
2,407 | 1 | Title: Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Abstract: The majority of commercial computers today are register machines of von Neumann type. We have developed a method to evolve Turing-complete programs for a register machine. The described implementation enables the use of... | [
1631,
2704
] | Test |
2,408 | 4 | Title: Exploratory Learning in the Game of GO
Abstract: This paper considers the importance of exploration to game-playing programs which learn by playing against opponents. The central question is whether a learning program should play the move which offers the best chance of winning the present game, or if it shoul... | [
523,
1975,
2145
] | Train |
2,409 | 2 | Title: Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm
Abstract: Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches ... | [
174,
477,
638,
1644,
1869,
2027,
2543,
2672
] | Train |
2,410 | 2 | Title: Subsymbolic Case-Role Analysis of Sentences with Embedded Clauses
Abstract: A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case-role ... | [
204,
1811,
2049
] | Validation |
2,411 | 4 | Title: Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Abstract: In this paper, we propose a memory-based Q-learning algorithm called predictive Q-routing (PQ-routing) for adaptive traffic control. We attempt to address two problems encountered in Q-routing (Boyan & Li... | [
2666
] | Train |
2,412 | 4 | Title: On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning
Abstract: Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the origi... | [
427,
1758,
2255
] | Train |
2,413 | 2 | Title: On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning
Abstract: Most connectionist modeling assumes noise-free inputs. This assumption is often violated. This paper introduces the idea of clearning, of simultaneously cleaning the data and learning the underlying structure. The cleanin... | [
668,
1366,
1718,
2239,
2373
] | Train |
2,414 | 2 | Title: On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning
Abstract: Most connectionist modeling assumes noise-free inputs. This assumption is often violated. This paper introduces the idea of clearning, of simultaneously cleaning the data and learning the underlying structure. The cleanin... | [
668,
1366,
1718,
2239,
2373
] | Test |
2,415 | 0 | Title: LEARNING MORE FROM LESS DATA: EXPERIMENTS WITH LIFELONG ROBOT LEARNING
Abstract: Most connectionist modeling assumes noise-free inputs. This assumption is often violated. This paper introduces the idea of clearning, of simultaneously cleaning the data and learning the underlying structure. The cleaning step ca... | [
1112,
2530
] | Train |
2,416 | 3 | Title: Wavelet Thresholding via a Bayesian Approach
Abstract: We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wav... | [
1910,
2375,
2506
] | Train |
2,417 | 3 | Title: Choice of Basis for Laplace Approximation
Abstract: Maximum a posteriori optimization of parameters and the Laplace approximation for the marginal likelihood are both basis-dependent methods. This note compares two choices of basis for models parameterized by probabilities, showing that it is possible to impro... | [
157,
2532
] | Train |
2,418 | 6 | Title: A Fast Algorithm for the Computation and Enumeration of Perfect Phylogenies
Abstract: The Perfect Phylogeny Problem is a classical problem in computational evolutionary biology, in which a set of species/taxa is described by a set of qualitative characters. In recent years, the problem has been shown to be NP-... | [
2141,
2345,
2511
] | Test |
2,419 | 3 | Title: Adaptive probabilistic networks
Abstract: Belief networks (or probabilistic networks) and neural networks are two forms of network representations that have been used in the development of intelligent systems in the field of artificial intelligence. Belief networks provide a concise representation of general p... | [
492,
1268,
2323
] | Validation |
2,420 | 3 | Title: A Parallel Learning Algorithm for Bayesian Inference Networks
Abstract: We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric, and a distributed, asynchronous, adaptive search technique called naggin... | [
1527,
2169
] | Train |
2,421 | 3 | Title: On Convergence of the EM Algorithm and the Gibbs Sampler SUMMARY
Abstract: In this article we investigate the relationship between the two popular algorithms, the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to tha... | [
74,
115,
263,
345,
1829,
1856,
1868,
1906,
2266,
2389,
2590,
2654
] | Validation |
2,422 | 2 | Title: Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory
Abstract: Underwater mammal sound classification is demonstrated using a novel application of wavelet time/frequency decomposition and feature extraction using a BCM unsupervised network. Different fea... | [
359,
2376,
2499,
2500
] | Train |
2,423 | 6 | Title: Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs
Abstract: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by study... | [
550,
853,
862,
881,
1161,
1191,
1601,
1644,
1732,
2225,
2627,
2657
] | Test |
2,424 | 5 | Title: Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract
Abstract: In this paper we give a completeness theorem of an inductive inference rule inverse entailment proposed by Muggleton. Our main result is that a hypothesis clause H can be derived from an example E under a background theory B wi... | [
1428,
2589
] | Train |
2,425 | 3 | Title: Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
Abstract: We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particul... | [
62,
423,
788,
2341,
2474
] | Test |
2,426 | 5 | Title: Inductive Constraint Logic
Abstract: A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or f... | [
344,
638,
1007,
1686,
1919,
2126,
2282,
2431,
2493
] | Train |
2,427 | 2 | Title: Solving the Multiple-Instance Problem with Axis-Parallel Rectangles
Abstract: The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be... | [
318,
507,
1888,
2391,
2394,
2548,
2591
] | Train |
2,428 | 3 | Title: Bumptrees for Efficient Function, Constraint, and Classification Learning
Abstract: A new class of data structures called bumptrees is described. These structures are useful for efficiently implementing a number of neural network related operations. An empirical comparison with radial basis functions is presen... | [
44,
116,
225,
760,
1860,
2021,
2042
] | Train |
2,429 | 1 | Title: Automatic Definition of Modular Neural Networks
Abstract: A new class of data structures called bumptrees is described. These structures are useful for efficiently implementing a number of neural network related operations. An empirical comparison with radial basis functions is presented on a robot arm mapping... | [
1143,
2152,
2281,
2317,
2624,
2667
] | Validation |
2,430 | 4 | Title: Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic
Abstract: Model learning combined with dynamic programming has been shown to be effective for learning control of continuous state dynamic systems. The simplest method assumes the lear... | [
294,
682,
1860,
2647
] | Train |
2,431 | 5 | Title: Multi-class problems and discretization in ICL Extended abstract
Abstract: Handling multi-class problems and real numbers is important in practical applications of machine learning to KDD problems. While attribute-value learners address these problems as a rule, very few ILP systems do so. The few ILP systems ... | [
426,
1919,
2426,
2591
] | Test |
2,432 | 4 | Title: Learning the Peg-into-Hole Assembly Operation with a Connectionist Reinforcement Technique
Abstract: The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better relati... | [
1755
] | Test |
2,433 | 2 | Title: Robust Sound Localization: An Application of an Auditory Perception System for a Humanoid Robot
Abstract: The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better r... | [
2437
] | Train |
2,434 | 3 | Title: Causal Inference from Indirect Experiments
Abstract: Indirect experiments are studies in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced to receive treatment programs. The purpose of this paper is to bring to the attention of experimental r... | [
105,
1326,
1747,
2069,
2160
] | Validation |
2,435 | 2 | Title: Identification in H 1 with Nonuniformly Spaced Frequency Response Measurements
Abstract: In this paper, the problem of "system identification in H 1 " is investigated in the case when the given frequency response data is not necessarily on a uniformly spaced grid of frequencies. A large class of robustly conve... | [
2236,
2262
] | Test |
2,436 | 5 | Title: Space-Time Scheduling of Instruction-Level Parallelism on a Raw Machine
Abstract: Advances in VLSI technology will enable chips with over a billion transistors within the next decade. Unfortunately, the centralized-resource architectures of modern microprocessors are ill-suited to exploit such advances. Achiev... | [
2649
] | Train |
2,437 | 4 | Title: Embodiment and Manipulation Learning Process for a Humanoid Hand
Abstract: Advances in VLSI technology will enable chips with over a billion transistors within the next decade. Unfortunately, the centralized-resource architectures of modern microprocessors are ill-suited to exploit such advances. Achieving a h... | [
427,
745,
2433
] | Train |
2,438 | 2 | Title: Integrating Initialization Bias and Search Bias in Neural Network Learning
Abstract: The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using... | [
92,
2091
] | Train |
2,439 | 2 | Title: Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages
Abstract: We consider recurrent analog neural nets where the output of each gate is subject to Gaussian noise, or any other common noise distribution that is nonzero on a large set. We show that ma... | [
407,
1875
] | Test |
2,440 | 2 | Title: Modifying Network Architectures for Certainty-Factor Rule-Base Revision
Abstract: This paper describes Rapture | a system for revising probabilistic rule bases that converts symbolic rules into a connectionist network, which is then trained via connectionist techniques. It uses a modified version of backpropag... | [
2543
] | Validation |
2,441 | 5 | Title: Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on
Abstract: This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimi... | [
66,
344,
2585
] | Train |
2,442 | 4 | Title: Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis
Abstract: Probability theory represents and manipulates uncertainties, but cannot tell us how to behave. For that we need utility theory which assigns values to the usefulness of different states, and decisio... | [
71,
523,
565,
929,
2118
] | Train |
2,443 | 3 | Title: Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier
Abstract: The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be sur... | [
1339,
2338,
2343
] | Train |
2,444 | 4 | Title: Symbiotic Evolution of Neural Networks in Sequential Decision Tasks
Abstract: The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations ... | [
500,
2257
] | Train |
2,445 | 2 | Title: Simulation of Reduced Precision Arithmetic for Digital Neural Networks Using the RAP Machine
Abstract: This paper describes some of our recent work in the development of computer architectures for efficient execution of artificial neural network algorithms. Our earlier system, the Ring Array Processor (RAP), w... | [
2268,
2275
] | Validation |
2,446 | 1 | Title: Simulation of Reduced Precision Arithmetic for Digital Neural Networks Using the RAP Machine
Abstract: 1] R.K. Belew, J. McInerney, and N. Schraudolph, Evolving networks: using the genetic algorithm with connectionist learning, in Artificial Life II, SFI Studies in the Science of Complexity, C.G. Langton, C. T... | [
129,
247,
2451
] | Test |
2,447 | 0 | Title: New Roles for Machine Learning in Design for Design of Educational Computing New roles for
Abstract: Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the de... | [
378,
2010
] | Train |
2,448 | 2 | Title: Automatic Smoothing Spline Projection Pursuit Automatic Smoothing Spline Projection Pursuit.
Abstract: kj = 1. The standard PPR algorithm of Friedman and Stuet-zle (1981) estimates the smooth functions f j using the supersmoother nonparametric scatterplot smoother. Friedman's algorithm constructs a model with... | [
427,
519,
2311,
2526
] | Train |
2,449 | 5 | Title: Learning by Refining Algorithm Sketches
Abstract: In this paper we suggest a mechanism that improves significantly the performance of a top-down inductive logic programming (ILP) learning system. This improvement is achieved at the cost of giving to the system extra information that is not difficult to formula... | [
1881,
2158,
2450
] | Train |
2,450 | 5 | Title: Architecture for Iterative Learning of Recursive Definitions
Abstract: In this paper we are concerned with the problem of inducing recursive Horn clauses from small sets of training examples. The method of iterative bootstrap induction is presented. In the first step, the system generates simple clauses, which... | [
1498,
1881,
2449
] | Train |
2,451 | 1 | Title: Automatic Design of Cellular Neural Networks by means of Genetic Algorithms: Finding a Feature Detector
Abstract: This paper aims to examine the use of genetic algorithms to optimize subsystems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an opt... | [
129,
163,
1973,
2446
] | Train |
2,452 | 2 | Title: A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series
Abstract: This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits... | [
1315,
2595
] | Train |
2,453 | 4 | Title: Packet Routing and Reinforcement Learning: Estimating Shortest Paths in Dynamic Graphs
Abstract: This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static... | [
427,
2666
] | Validation |
2,454 | 2 | Title: Early Stopping but when?
Abstract: Validation can be used to detect when overfitting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overfitting ("early stopping"). The exact criterion used for validation-based early stopping, however, is usually ... | [
881,
1058,
1342,
2129
] | Train |
2,455 | 6 | Title: Learning From a Population of Hypotheses
Abstract: We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the ques... | [
453,
456,
2354
] | Train |
2,456 | 3 | Title: Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Abstract: Markov chain Monte Carlo (MCMC) methods make possible the use of flexible Bayesian models that would otherwise be computationally infeasible. In recent years, a great variety of such applications have been described in the literature. Appl... | [
41,
48,
1029,
1742
] | Train |
2,457 | 2 | Title: In Proceedings of the 1997 Sian Kaan International Workshop on Neural Networks and Neurocontrol. Real-Valued
Abstract: 2 Neural Network & Machine Learning Laboratory Computer Science Department Brigham Young University Provo, UT 84602, USA Email: martinez@cs.byu.edu WWW: http://axon.cs.byu.edu Abstract. Many neu... | [
690,
1980
] | Train |
2,458 | 3 | Title: ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS
Abstract: A large body of nonparametric statistical literature is devoted to density estimation. Overviews are given in Silverman (1986) and Izenman (1991). This paper addresses the problem of univariate density estimation in a novel way.... | [
2242,
2366,
2506
] | Validation |
2,459 | 2 | Title: Control of Selective Visual Attention: Modeling the "Where" Pathway
Abstract: Intermediate and higher vision processes require selection of a subset of the available sensory information before further processing. Usually, this selection is implemented in the form of a spatially circumscribed region of the visu... | [
553,
2606
] | Train |
2,460 | 3 | Title: Mutual Information as a Bayesian Measure of Independence
Abstract: 0.0 Abstract. The problem of hypothesis testing is examined from both the historical and the Bayesian points of view in the case that sampling is from an underlying joint probability distribution and the hypotheses tested for are those of indep... | [
2384
] | Train |
2,461 | 3 | Title: A guide to the literature on learning probabilistic networks from data
Abstract: This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the stat... | [
1076,
1078,
2492
] | Validation |
2,462 | 3 | Title: Building Classifiers using Bayesian Networks
Abstract: Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the ques... | [
401,
1986
] | Validation |
2,463 | 3 | Title: Learning Belief Networks in the Presence of Missing Values and Hidden Variables
Abstract: In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both network structure and... | [
558,
1934
] | Train |
2,464 | 3 | Title: Static Data Association with a Terrain-Based Prior Density
Abstract: In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both network structure and parameters from comp... | [
1817
] | Test |
2,465 | 0 | Title: A systematic description of greedy optimisation algorithms for cost sensitive generalisation
Abstract: This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees ... | [
228,
638,
2057
] | Train |
2,466 | 0 | Title: FLARE: Induction with Prior Knowledge
Abstract: This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- a... | [
831,
1830,
2240
] | Validation |
2,467 | 6 | Title: Learning to Reason with a Restricted View
Abstract: The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge. Computational considerations show that this is a useful paradig... | [
323,
2155,
2468
] | Train |
2,468 | 3 | Title: On the Hardness of Approximate Reasoning
Abstract: Many AI problems, when formalized, reduce to evaluating the probability that a propositional expression is true. In this paper we show that this problem is computationally intractable even in surprisingly restricted cases and even if we settle for an approxima... | [
2467
] | Validation |
2,469 | 2 | Title: Extended Kalman filter in recurrent neural network training and pruning
Abstract: Recently, extended Kalman filter (EKF) based training has been demonstrated to be effective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet... | [
1789
] | Train |
2,470 | 1 | Title: Induction and Recapitulation of Deep Musical Structure
Abstract: We describe recent extensions to our framework for the automatic generation of music-making programs. We have previously used genetic programming techniques to produce music-making programs that satisfy user-provided critical criteria. In this pa... | [
1230,
1277,
2101,
2643,
2646
] | Test |
2,471 | 0 | Title: ILA: Combining Inductive Learning with Prior Knowledge and Reasoning
Abstract: We describe recent extensions to our framework for the automatic generation of music-making programs. We have previously used genetic programming techniques to produce music-making programs that satisfy user-provided critical criter... | [
2245
] | Train |
2,472 | 4 | Title: Toward an Ideal Trainer*
Abstract: This paper appeared in 1994 in Machine Learning, 15 (3): 251-277. Abstract This paper demonstrates how the nature of the opposition during training affects learning to play two-person, perfect information board games. It considers different kinds of competitive training, the ... | [
565,
2476
] | Test |
2,473 | 0 | Title: A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a
Abstract: The negative effect is naturally more significant in the more complex domain. The graph for the simple domain crosses the 0 line earlier than the complex domain. That means that learning starts to be useful wi... | [
434,
551,
1192,
2551
] | Train |
2,474 | 3 | Title: The Frame Problem and Bayesian Network Action Representations
Abstract: We examine a number of techniques for representing actions with stochastic effects using Bayesian networks and influence diagrams. We compare these techniques according to ease of specification and size of the representation required for t... | [
62,
2078,
2425
] | Train |
2,475 | 6 | Title: Learning polynomials with queries: The highly noisy case task for the case when F
Abstract: Given a function f mapping n-variate inputs from a finite field F into F , we consider the task of reconstructing a list of all n-variate degree d polynomials which agree with f on a tiny but non-negligible fraction, f... | [
574,
591,
640,
1363,
2246
] | Validation |
2,476 | 6 | Title: Why Experimentation can be better than "Perfect Guidance"
Abstract: Many problems correspond to the classical control task of determining the appropriate control action to take, given some (sequence of) observations. One standard approach to learning these control rules, called behavior cloning, involves watch... | [
294,
2472
] | Validation |
2,477 | 2 | Title: Neural Models for Part-Whole Hierarchies
Abstract: We present a connectionist method for representing images that explicitly addresses their hierarchical nature. It blends data from neu-roscience about whole-object viewpoint sensitive cells in inferotem-poral cortex 8 and attentional basis-field modulation in ... | [
2678
] | Train |
2,478 | 1 | Title: Culture Enhances the Evolvability of Cognition
Abstract: This paper discusses the role of culture in the evolution of cognitive systems. We define culture as any information transmitted between individuals and between generations by non-genetic means. Experiments are presented that use genetic programming syst... | [
2220,
2226
] | Test |
2,479 | 1 | Title: A Transformation System for Interactive Reformulation of Design Optimization Strategies
Abstract: Numerical design optimization algorithms are highly sensitive to the particular formulation of the optimization problems they are given. The formulation of the search space, the objective function and the constrai... | [
2128,
2319
] | Train |
2,480 | 4 | Title: Planning by Incremental Dynamic Programming
Abstract: This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incr... | [
523,
565,
566,
653,
2485
] | Train |
2,481 | 6 | Title: The Design and Evaluation of a Rule Induction Algorithm
Abstract: technical report BYU-CS-93-11 June 1993 | [
1858
] | Test |
2,482 | 0 | Title: CBR for Document Retrieval: The FAllQ Project
Abstract: This paper reports about a project on document retrieval in an industrial setting. The objective is to provide a tool that helps finding documents related to a given query, such as answers in Frequently Asked Questions databases. A CBR approach has been u... | [
1854,
1855,
2123,
2645
] | Train |
2,483 | 6 | Title: How Many Queries are Needed to Learn?
Abstract: We investigate the query complexity of exact learning in the membership and (proper) equivalence query model. We give a complete characterization of concept classes that are learnable with a polynomial number of polynomial sized queries in this model. We give app... | [
1003,
1004,
1848
] | Test |
2,484 | 0 | Title: The evaluation of Anapron: A case study in evaluating a case-based system
Abstract: This paper presents a case study in evaluating a case-based system. It describes the evaluation of Anapron, a system that pronounces names by a combination of rule-based and case-based reasoning. Three sets of experiments were ... | [
986,
1644,
2614,
2616
] | Test |
2,485 | 4 | Title: Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions
Abstract: Consider a given value function on states of a Markov decision problem, as might result from applying a reinforcement learning algorithm. Unless this value function equals the corresponding optimal value function, at some ... | [
173,
565,
566,
575,
749,
1378,
1816,
2480
] | Train |
2,486 | 2 | Title: The Canonical Distortion Measure for Vector Quantization and Function Approximation
Abstract: To measure the quality of a set of vector quantization points a means of measuring the distance between a random point and its quantization is required. Common metrics such as the Hamming and Euclidean metrics, while ... | [
1970,
2586
] | Train |
2,487 | 6 | Title: An Optimized Theory Revision Module
Abstract: Theory revision systems typically use a set of theory-to-theory transformations f k g to hill-climb from a given initial theory to a new theory whose empirical accuracy, over a given set of labeled training instances fc j g, is a local maximum. At the heart of each... | [
52,
136,
430,
1823
] | Train |
2,488 | 3 | Title: Density and hazard rate estimation for right censored data using wavelet methods
Abstract: This paper describes a wavelet method for the estimation of density and hazard rate functions from randomly right censored data. We adopt a nonparametric approach in assuming that the density and hazard rate have no spec... | [
1910
] | Train |
2,489 | 0 | Title: BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES
Abstract: Experience plays an important role in the development of human expertise. One computational model of how experience affects expertise is provided by research on case-based reasoning, which examines how stored cases encapsulating tr... | [
643,
1126,
2371,
2372
] | Train |
2,490 | 1 | Title: Computer Evolution of Buildable Objects for Evolutionary Design by Computers
Abstract: Experience plays an important role in the development of human expertise. One computational model of how experience affects expertise is provided by research on case-based reasoning, which examines how stored cases encapsula... | [
1807
] | Train |
2,491 | 2 | Title: Prosopagnosia in Modular Neural Network Models
Abstract: There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex obje... | [
1981
] | Train |
2,492 | 3 | Title: Robust Parameter Learning in Bayesian Networks with Missing Data
Abstract: There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kin... | [
577,
1900,
2461,
2547
] | Test |
2,493 | 5 | Title: Relational Knowledge Discovery in Databases
Abstract: In this paper, we indicate some possible applications of ILP or similar techniques in the knowledge discovery field, and then discuss several methods for adapting and linking ILP-systems to relational database systems. The proposed methods range from "pure ... | [
1428,
2426
] | Train |
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