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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
94	3	Title: Perfect Simulation in Stochastic Geometry Abstract: Simulation plays an important role in stochastic geometry and related fields, because all but the simplest random set models tend to be intractable to analysis. Many simulation algorithms deliver (approximate) samples of such random set models, for example by...	[ 41, 126 ]	Test
95	3	Title: Bayesian Detection of Clusters and Discontinuities in Disease Maps Abstract: Simulation plays an important role in stochastic geometry and related fields, because all but the simplest random set models tend to be intractable to analysis. Many simulation algorithms deliver (approximate) samples of such random s...	[ 161, 358, 759, 1255 ]	Validation
96	0	Title: Lazy Induction Triggered by CBR Abstract: In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combining CBR and induction techniques aiming at verifying the knowledge and/or building an efficient cas...	[ 438, 478, 649, 2061 ]	Train
97	2	Title: Adaptive Tuning of Numerical Weather Prediction Models: Simultaneous Estimation of Weighting, Smoothing and Physical Parameters 1 Abstract: In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combini...	[ 439 ]	Validation
98	6	Title: Planning and Learning in an Adversarial Robotic Game Abstract: 1 This paper demonstrates the tandem use of a finite automata learning algorithm and a utility planner for an adversarial robotic domain. For many applications, robot agents need to predict the movement of objects in the environment and plan to avo...	[ 615, 1954, 2696 ]	Train
99	3	Title: Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models Abstract: Claudia Cargnoni is with the Dipartimento Statistico, Universita di Firenze, 50100 Firenze, Italy. Peter Muller is Assistant Professor, and Mike West is Professor, in the Institute of Statistics and Decision...	[ 759, 1255, 1613, 1619, 1722, 1803, 1852, 2578, 2592, 2679 ]	Test
100	1	Title: Using Markov Chains to Analyze GAFOs Abstract: Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics ...	[ 265, 758, 1611 ]	Test
101	2	Title: Adaptive Noise Injection for Input Variables Relevance Determination Abstract: In this paper we consider the application of training with noise in multi-layer perceptron to input variables relevance determination. Noise injection is modified in order to penalize irrelevant features. The proposed algorithm is a...	[ 331, 1112, 2680, 2686 ]	Validation
102	2	Title: Multivariate versus Univariate Decision Trees Abstract: COINS Technical Report 92-8 January 1992 Abstract In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a decision tree by training a linear machine and t...	[ 404, 1824, 1893, 1895, 1964, 2012, 2333, 2583 ]	Train
103	4	Title: NEUROCONTROL BY REINFORCEMENT LEARNING Abstract: Reinforcement learning (RL) is a model-free tuning and adaptation method for control of dynamic systems. Contrary to supervised learning, based usually on gradient descent techniques, RL does not require any model or sensitivity function of the process. Hence, R...	[ 128, 294, 465, 471, 565 ]	Validation
104	2	Title: How Lateral Interaction Develops in a Self-Organizing Feature Map Abstract: A biologically motivated mechanism for self-organizing a neural network with modifiable lateral connections is presented. The weight modification rules are purely activity-dependent, unsupervised and local. The lateral interaction weig...	[ 747, 771 ]	Train
105	3	Title: The New Challenge: From a Century of Statistics to an Age of Causation Abstract: Some of the main users of statistical methods - economists, social scientists, and epidemiologists are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the yea...	[ 248, 1326, 2434 ]	Validation
106	5	Title: Combining Top-down and Bottom-up Techniques in Inductive Logic Programming Abstract: This paper describes a new method for inducing logic programs from examples which attempts to integrate the best aspects of existing ILP methods into a single coherent framework. In particular, it combines a bottom-up method s...	[ 597 ]	Train
107	3	Title: Computing upper and lower bounds on likelihoods in intractable networks Abstract: We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of the network (or clique size) precludes exact com...	[ 108, 250, 498, 898, 1288, 1937 ]	Train
108	3	Title: Recursive algorithms for approximating probabilities in graphical models Abstract: MIT Computational Cognitive Science Technical Report 9604 Abstract We develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Y...	[ 107, 250, 304, 498, 898, 1288 ]	Train
109	6	Title: A General Lower Bound on the Number of Examples Needed for Learning Abstract: We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy...	[ 171, 459, 488, 507, 535, 635, 640, 672, 778, 884, 955, 967, 1074, 1296, 1661, 1888, 2053, 2054, 2315 ]	Train
110	2	Title: Data Exploration Using Self-Organizing Maps Abstract: We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy and confidence paramete...	[ 687, 745, 747 ]	Validation
111	2	Title: Tau Net: A Neural Network for Modeling Temporal Variability Abstract: The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance t...	[ 350 ]	Validation
112	2	Title: Interpretable Neural Networks with BP-SOM Abstract: Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that offers possibilities to overcome this difficulty. It is sho...	[ 572, 624, 747, 881 ]	Test
113	2	Title: LU TP Pattern Discrimination Using Feed-Forward Networks a Benchmark Study of Scaling Behaviour Abstract: The discrimination powers of Multilayer perceptron (MLP) and Learning Vector Quantisation (LVQ) networks are compared for overlapping Gaussian distributions. It is shown, both analytically and with Monte ...	[ 747 ]	Train
114	6	Title: A Generalization of Sauer's Lemma Abstract: The discrimination powers of Multilayer perceptron (MLP) and Learning Vector Quantisation (LVQ) networks are compared for overlapping Gaussian distributions. It is shown, both analytically and with Monte Carlo studies, that the MLP network handles high dimensional pr...	[ 171 ]	Train
115	3	Title: Rate of Convergence of the Gibbs Sampler by Gaussian Approximation SUMMARY Abstract: In this article we approximate the rate of convergence of the Gibbs sampler by a normal approximation of the target distribution. Based on this approximation, we consider many implementational issues for the Gibbs sampler, e....	[ 41, 138, 904, 1713, 2153, 2421 ]	Validation
116	0	Title: Rate of Convergence of the Gibbs Sampler by Gaussian Approximation SUMMARY Abstract: Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database fo...	[ 88, 686, 2428 ]	Validation
117	3	Title: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis 1 Abstract: Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the ...	[ 155, 345, 452, 513 ]	Train
118	4	Title: Learning to Race: Experiments with a Simulated Race Car Abstract: We have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. We have found that separating the layers has expedited gradually improving competition and mult-agent int...	[ 465, 565, 636 ]	Train
119	5	Title: Cost-sensitive feature reduction applied to a hybrid genetic algorithm Abstract: This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be elimina...	[ 228, 430, 686 ]	Validation
120	1	Title: Genetic Programming Exploratory Power and the Discovery of Functions Abstract: Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an anal...	[ 188, 1184, 1959, 2175 ]	Validation
121	2	Title: LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992) Abstract: Previous neural network learning algorithms for sequence processing are computationally expensive and perform poorly when it comes to long time lags. This paper first introduces a ...	[ 595, 731, 1825, 1990 ]	Train
122	2	Title: Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex Abstract: Previous neural network learning algorithms for sequence processing are computationally expensive and perform poorly when it comes to long time lags. This paper first introduces a simple principle for reducing the descriptions ...	[ 124, 127, 1093, 2400 ]	Train
123	2	Title: Fast Numerical Integration of Relaxation Oscillator Networks Based on Singular Limit Solutions Abstract: Relaxation oscillations exhibiting more than one time scale arise naturally from many physical systems. This paper proposes a method to numerically integrate large systems of relaxation oscillators. The num...	[ 553 ]	Validation
124	2	Title: Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons Abstract: The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such a...	[ 122, 127, 745, 747, 2400 ]	Train
125	2	Title: Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo Abstract: The full Bayesian method for applying neural networks to a prediction problem is to set up the prior/hyperprior structure for the net and then perform the necessary integrals. However, these integrals are not tractable analytically...	[ 160, 394, 1857 ]	Train
126	3	Title: Perfect Simulation of some Point Processes for the Impatient User Abstract: Recently Propp and Wilson [14] have proposed an algorithm, called Coupling from the Past (CFTP), which allows not only an approximate but perfect (i.e. exact) simulation of the stationary distribution of certain finite state space Mark...	[ 94, 2208 ]	Test
127	2	Title: Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex Abstract: Cells in the visual cortex are selective not only to ocular dominance and orientation of the input, but also to its size and spatial frequency. The simulations reported in this pap...	[ 18, 122, 124, 745, 747, 2228, 2400 ]	Validation
128	4	Title: Optimal Attitude Control of Satellites by Artificial Neural Networks: a Pilot Study Abstract: A pilot study is described on the practical application of artificial neural networks. The limit cycle of the attitude control of a satellite is selected as the test case. One of the sources of the limit cycle is a po...	[ 103, 294, 565 ]	Train
129	1	Title: Evolving Networks: Using the Genetic Algorithm with Connectionist Learning Abstract: A pilot study is described on the practical application of artificial neural networks. The limit cycle of the attitude control of a satellite is selected as the test case. One of the sources of the limit cycle is a position de...	[ 15, 22, 163, 188, 538, 1204, 1409, 1728, 1973, 2165, 2193, 2220, 2363, 2446, 2451 ]	Validation
130	6	Title: PAC-Learning PROLOG clauses with or without errors Abstract: In a nutshell we can describe a generic ILP problem as following: given a set E of (positive and negative) examples of a target predicate, and some background knowledge B about the world (usually a logic program including facts and auxiliary predicat...	[ 459, 640, 672 ]	Test
131	3	Title: The Expectation-Maximization Algorithm for MAP Estimation Abstract: The Expectation-Maximization algorithm given by Dempster et al (1977) has enjoyed considerable popularity for solving MAP estimation problems. This note gives a simple derivation of the algorithm, due to Luttrell (1994), that better illustrate...	[ 76 ]	Validation
132	4	Title: TOWARDS PLANNING: INCREMENTAL INVESTIGATIONS INTO ADAPTIVE ROBOT CONTROL Abstract: The Expectation-Maximization algorithm given by Dempster et al (1977) has enjoyed considerable popularity for solving MAP estimation problems. This note gives a simple derivation of the algorithm, due to Luttrell (1994), that be...	[ 346 ]	Train
133	2	Title: PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1 Abstract: We consider the problem of how to incorporate prior knowledge in supervised learning techniques. We set the problem in the framework of regularization theory, and consider the case in which we know that the approximated functio...	[ 394, 608, 611 ]	Train
134	2	Title: Gain Adaptation Beats Least Squares? Abstract: I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithm...	[ 505, 1118, 1782, 2135 ]	Validation
135	5	Title: More Efficient Windowing Abstract: Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning ...	[ 418, 654 ]	Train
136	5	Title: Theory Refinement Combining Analytical and Empirical Methods Abstract: This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory....	[ 92, 159, 244, 479, 986, 1102, 1259, 1370, 1413, 1479, 1539, 1776, 2172, 2231, 2399, 2487, 2543, 2580, 2635 ]	Test
137	3	Title: Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications Abstract: Suppose one wishes to sample from the density (x) using Markov chain Monte Carlo (MCMC). An auxiliary variable u and its conditional distribution (ujx) can be defined, giving the joint distribution (x; u) = (x)(ujx). A MCMC sch...	[ 138, 416, 748 ]	Train
138	3	Title: Convergence properties of perturbed Markov chains Abstract: Acknowledgements. We thank Neal Madras, Radford Neal, Peter Rosenthal, and Richard Tweedie for helpful conversations. This work was partially supported by EPSRC of the U.K., and by NSERC of Canada.	[ 115, 137, 416, 748, 892, 1713, 1716 ]	Train
139	1	Title: Investigating the Generality of Automatically Defined Functions Abstract: This paper studies how well the combination of simulated annealing and ADFs solves genetic programming (GP) style program discovery problems. On a suite composed of the even-k-parity problems for k = 3,4,5, it analyses the performance of...	[ 188, 2361 ]	Train
140	6	Title: Exploiting the Omission of Irrelevant Data Abstract: Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that...	[ 323 ]	Train
141	1	Title: Hierarchical Self-Organization in Genetic Programming Abstract: This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally ada...	[ 163, 188, 1184 ]	Train
142	2	Title: PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS Abstract: A decision problem associated with a fundamental nonconvex model for linearly inseparable pattern sets is shown to be NP-complete. Another nonconvex model that employs an 1 norm instead of the 2-norm, can be solved...	[ 227, 230, 391, 520, 823, 1283, 1318, 1547 ]	Train
143	2	Title: RESONANCE AND THE PERCEPTION OF MUSICAL METER Abstract: Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structure, when considering the percep...	[ 180, 201, 337, 346, 363 ]	Train
144	2	Title: The Observers Paradox: Apparent Computational Complexity in Physical Systems Abstract: Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structu...	[ 188, 444, 2102 ]	Train
145	1	Title: LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH Abstract: Over the years there has been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GENESIS. A few have adopted the steady-state...	[ 163, 390, 1218, 1224, 1530, 1646, 2248, 2251, 2280, 2286, 2296, 2521 ]	Train
146	2	Title: Convergence-Zone Episodic Memory: Analysis and Simulations Abstract: Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, t...	[ 17, 427, 747 ]	Test
147	0	Title: Convergence-Zone Episodic Memory: Analysis and Simulations Abstract: Empirical Learning Results in POLLYANNA The value of empirical learning is demonstrated by results of testing the theory space search (TSS) component of POLLYANNA. Empirical data shows approximations generated from generic simplifying assumpt...	[ 479 ]	Train
148	4	Title: Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm Abstract: In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-...	[ 210, 460, 656, 1649, 1687 ]	Train
149	0	Title: Corporate Memories as Distributed Case Libraries Abstract: Rising operating costs and structural transformations such as resizing and globaliza-tion of companies all over the world have brought into focus the emerging discipline of knowledge management that is concerned with making knowledge pay off. Corporate...	[ 66 ]	Train
150	0	Title: Using Knowledge of Cognitive Behavior to Learn from Failure Abstract: When learning from reasoning failures, knowledge of how a system behaves is a powerful lever for deciding what went wrong with the system and in deciding what the system needs to learn. A number of benefits arise when systems possess knowled...	[ 50, 643 ]	Validation
151	6	Title: Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach Abstract: Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and ot...	[ 360, 1528, 1595 ]	Test
152	2	Title: Replicability of Neural Computing Experiments Abstract: If an experiment requires statistical analysis to establish a result, then one should do a better experiment. Ernest Rutherford, 1930 Most proponents of cold fusion reporting excess heat from their electrolysis experiments were claiming that one of the ma...	[ 15, 1383 ]	Train
153	2	Title: Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques Abstract: In this paper, we propose an unsupervised neural network allowing a robot to learn sensori-motor associations with a delayed reward. The robot task is to learn the "meaning" of pictograms...	[ 63, 294, 747 ]	Test
154	2	Title: Data-driven Modeling and Synthesis of Acoustical Instruments Abstract: We present a framework for the analysis and synthesis of acoustical instruments based on data-driven probabilistic inference modeling. Audio time series and boundary conditions of a played instrument are recorded and the non-linear mapping ...	[ 74, 392 ]	Train
155	3	Title: Inference in Model-Based Cluster Analysis Abstract: Technical Report no. 285 Department of Statistics University of Washington. March 10, 1995	[ 12, 84, 117, 513 ]	Train
156	5	Title: Structural Regression Trees Abstract: In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relat...	[ 236, 314, 431, 521 ]	Train
157	6	Title: A Practical Bayesian Framework for Backprop Networks Abstract: A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping...	[ 78, 181, 214, 371, 393, 560, 716, 740, 766, 897, 916, 979, 1017, 1038, 1075, 1289, 1340, 1375, 1452, 1550, 1637, 1718, 2019, 2021, 2095, 2230, 2417, 2540, 2680 ]	Train
158	5	Title: Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor Abstract: Simultaneous multithreading is a technique that permits multiple independent threads to issue multiple instructions each cycle. In previous work we demonstrated the performance potential of simult...	[ 184, 433, 598, 707 ]	Validation
159	6	Title: Bias-Driven Revision of Logical Domain Theories Abstract: The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositiona...	[ 136, 985, 2066, 2172, 2543, 2674 ]	Train
160	2	Title: EVALUATION OF GAUSSIAN PROCESSES AND OTHER METHODS FOR NON-LINEAR REGRESSION Abstract: The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revi...	[ 125, 322, 469, 1857, 2540, 2681 ]	Test
161	3	Title: On Bayesian analysis of mixtures with an unknown number of components Summary Abstract: New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods, that are capable of jumping between the parameter subspaces corresponding to different numbe...	[ 95, 684, 713, 759, 996, 1147, 2311 ]	Train
162	4	Title: Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Critic Learning Systems Abstract: Northeastern University College of Computer Science Technical Report NU-CCS-93-11 fl We gratefully acknowledge the substantial contributions to this effort provided by Andy Barto,...	[ 173, 775 ]	Train
163	1	Title: 4 Implementing Application Specific Routines Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. Abstract: To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional v...	[ 22, 42, 55, 129, 141, 145, 174, 188, 189, 191, 219, 237, 266, 290, 309, 346, 380, 390, 395, 402, 415, 422, 448, 523, 530, 546, 563, 602, 606, 624, 658, 659, 689, 714, 717, 727, 743, 744, 757, 765, 769, 781, 793, 800, 813, 856,...	Test
164	6	Title: Improving Generalization with Active Learning Abstract: Active learning differs from passive "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful that...	[ 517, 740 ]	Test
165	5	Title: d d The Effects of Predicated Execution on Branch Prediction Abstract: This paper analyzes a variety of existing predication models for eliminating branch operations, and the effect that this elimination has on the branch prediction schemes in existing processors, including single issue architectures with simp...	[ 432 ]	Validation
166	0	Title: Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a Abstract: This paper describes a model of the complementarity of rules and precedents in the classification task. Under this model, precedents assist rule-based reasoning by operationalizing abstract ru...	[ 457, 649, 1125 ]	Test
167	4	Title: Auto-exploratory Average Reward Reinforcement Learning Abstract: We introduce a model-based average reward Reinforcement Learning method called H-learning and compare it with its discounted counterpart, Adaptive Real-Time Dynamic Programming, in a simulated robot scheduling task. We also introduce an extension...	[ 552, 554, 559, 1459 ]	Validation
168	1	Title: Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques Abstract: This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We ...	[ 475, 1728, 1756, 2604 ]	Test
169	2	Title: LEARNING LINEAR, SPARSE, FACTORIAL CODES Abstract: In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, and bandpass (i.e., wavelet-like). This note...	[ 212, 570 ]	Train
170	3	Title: Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence a free parameter. The Abstract: We study layered belief networks of binary random variables in which the conditional probabilities Pr[childjparents] depend monotonically on weighted sums of the parents. For these networks,...	[ 4, 250 ]	Train
171	6	Title: Characterizations of Learnability for Classes of f0; ng-valued Functions Abstract: We study layered belief networks of binary random variables in which the conditional probabilities Pr[childjparents] depend monotonically on weighted sums of the parents. For these networks, we give efficient algorithms for comp...	[ 109, 114 ]	Train
172	0	Title: Efficient Feature Selection in Conceptual Clustering Abstract: Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised lear...	[ 245, 430, 635 ]	Train
173	4	Title: An Upper Bound on the Loss from Approximate Optimal-Value Functions Abstract: Many reinforcement learning (RL) approaches can be formulated from the theory of Markov decision processes and the associated method of dynamic programming (DP). The value of this theoretical understanding, however, is tempered by ma...	[ 162, 294, 552, 565, 566, 575, 1378, 2485 ]	Train
174	2	Title: Symbolic and Subsymbolic Learning for Vision: Some Possibilities Abstract: Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3-dimensional objects require an adequate armamentarium of representations and learning mechanisms. This paper briefly ana...	[ 163, 501, 503, 2409 ]	Test
175	2	Title: SARDNET: A Self-Organizing Feature Map for Sequences Abstract: A self-organizing neural network for sequence classification called SARDNET is described and analyzed experimentally. SARDNET extends the Kohonen Feature Map architecture with activation retention and decay in order to create unique distributed res...	[ 747 ]	Train
176	5	Title: Knowledge Integration and Learning Abstract: LIACC - Technical Report 91-1 Abstract. In this paper we address the problem of acquiring knowledge by integration . Our aim is to construct an integrated knowledge base from several separate sources. The objective of integration is to construct one system that expl...	[ 379, 756 ]	Test
177	6	Title: Evaluation and Selection of Biases in Machine Learning Abstract: In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias space...	[ 430, 635, 885, 911, 965, 1489, 1959, 2192 ]	Train
178	5	Title: Learning Decision Trees from Decision Rules: Abstract: A method and initial results from a comparative study ABSTRACT A standard approach to determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit d...	[ 286, 378 ]	Train
179	2	Title: for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors, Abstract: OGI CSE Technical Report 96-006 Abstract: Smoothing regularizers for radial basis functions have been studied extensively, but no general smoothing regularizers for projective basis functions (PBFs),...	[ 331, 608, 611 ]	Train
180	2	Title: REDUCED MEMORY REPRESENTATIONS FOR MUSIC Abstract: We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events ...	[ 143, 350 ]	Test
181	6	Title: Ensemble Learning and Evidence Maximization Abstract: Ensemble learning by variational free energy minimization is a tool introduced to neural networks by Hinton and van Camp in which learning is described in terms of the optimization of an ensemble of parameter vectors. The optimized ensemble is an approximat...	[ 76, 157, 518, 662, 766 ]	Validation
182	3	Title: Adaptation for Self Regenerative MCMC SUMMARY Abstract: The self regenerative MCMC is a tool for constructing a Markov chain with a given stationary distribution by constructing an auxiliary chain with some other stationary distribution . Elements of the auxiliary chain are picked a suitable random number of ...	[ 468, 491 ]	Train
183	0	Title: Conceptual Analogy Abstract: Conceptual analogy (CA) is an approach that integrates conceptualization, i.e., memory organization based on prior experiences and analogical reasoning (Borner 1994a). It was implemented prototypically and tested to support the design process in building engineering (Borner and Jan...	[ 66, 454, 539, 541 ]	Validation
184	5	Title: Multipath Execution: Opportunities and Limits Abstract: Even sophisticated branch-prediction techniques necessarily suffer some mispredictions, and even relatively small mispredict rates hurt performance substantially in current-generation processors. In this paper, we investigate schemes for improving perform...	[ 158, 428, 432, 433 ]	Train
185	3	Title: Robustness Analysis of Bayesian Networks with Global Neighborhoods Abstract: This paper presents algorithms for robustness analysis of Bayesian networks with global neighborhoods. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. We present...	[ 324, 332, 389 ]	Train
186	4	Title: Adaptive state space quantisation: adding and removing neurons Abstract: This paper describes a self-learning control system for a mobile robot. Based on local sensor data, a robot is taught to avoid collisions with obstacles. The only feedback to the control system is a binary-valued external reinforcement si...	[ 294, 566, 588, 747 ]	Train
187	2	Title: Evaluation and Ordering of Rules Extracted from Feedforward Networks Abstract: Rules extracted from trained feedforward networks can be used for explanation, validation, and cross-referencing of network output decisions. This paper introduces a rule evaluation and ordering mechanism that orders rules extracted...	[ 462 ]	Train
188	1	Title: Coevolving High-Level Representations Abstract: Rules extracted from trained feedforward networks can be used for explanation, validation, and cross-referencing of network output decisions. This paper introduces a rule evaluation and ordering mechanism that orders rules extracted from feedforward networks base...	[ 42, 120, 129, 139, 141, 144, 163, 189, 262, 380, 415, 523, 717, 721, 755, 757 ]	Validation
189	1	Title: An Evolutionary Algorithm that Constructs Recurrent Neural Networks Abstract: Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure ...	[ 42, 163, 188, 395, 2102, 2664 ]	Train
190	2	Title: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones Abstract: Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between ...	[ 356, 519, 2223 ]	Train
191	1	Title: USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR Abstract: A new mechanism for genetic encoding of neural networks is proposed, which is loosely based on the marker structure of biological DNA. The mechanism allows all aspects of the network structure, including the numbe...	[ 22, 163, 294, 395, 448 ]	Train
192	2	Title: Smoothing Spline ANOVA with Component-Wise Bayesian "Confidence Intervals" To Appear, J. Computational and Graphical Statistics Abstract: We study a multivariate smoothing spline estimate of a function of several variables, based on an ANOVA decomposition as sums of main effect functions (of one variable), two...	[ 10, 193, 280, 420, 439, 510, 519, 705 ]	Train
193	2	Title: Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance Abstract: We study a multivariate smoothing spline estimate of a function of several variables, based on an ANOVA decomposition as sums of main effect functions (of one variable), two-factor inte...	[ 10, 74, 192, 238, 280, 420, 519, 2549 ]	Validation

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