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|---|---|---|---|---|
1,694 | 1 | Title: Strategy Adaptation by Competing Subpopulations
Abstract: The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more efficient. Each subpopulation uses a differ... | [
793,
1455
] | Train |
1,695 | 0 | Title: Analogical Problem Solving by Adaptation of Schemes
Abstract: We present a computational approach to the acquisition of problem schemes by learning by doing and to their application in analogical problem solving. Our work has its background in automatic program construction and relies on the concept of recursi... | [
1354
] | Train |
1,696 | 1 | Title: The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance
Abstract: Genetic algorithms (GAs) play a major role in many artificial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types ... | [
163,
1114,
1334,
1769,
1771,
1872,
1943,
1971,
2175,
2250,
2330
] | Train |
1,697 | 2 | Title: Neural Network Exploration Using Optimal Experiment Design
Abstract: We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the q... | [
16,
804,
929,
1559,
1599,
1664,
1667,
1683,
1703
] | Train |
1,698 | 0 | Title: CBET: a Case Base Exploration Tool
Abstract: We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of... | [
66,
430,
686,
1626,
2597
] | Train |
1,699 | 0 | Title: On the Usefulness of Re-using Diagnostic Solutions
Abstract: Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the sa... | [
799,
1215,
2656
] | Test |
1,700 | 2 | Title: Growing a Hypercubical Output Space in a Self-Organizing Feature Map
Abstract: Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is t... | [
687,
745,
1157
] | Validation |
1,701 | 2 | Title: Pattern analysis and synthesis in attractor neural networks
Abstract: The representation of hidden variable models by attractor neural networks is studied. Memories are stored in a dynamical attractor that is a continuous manifold of fixed points, as illustrated by linear and nonlinear networks with hidden neu... | [
33,
36,
832,
1591
] | Test |
1,702 | 6 | Title: Decision Graphs An Extension of Decision Trees
Abstract: Technical Report No: 92/173 (C) Jonathan Oliver 1992 Shortened appeared in AI and Statistics 1993[14] Abstract: In this paper, we examine Decision Graphs, a generalization of decision trees. We present an inference scheme to construct decision graphs usi... | [
1161,
1199
] | Train |
1,703 | 4 | Title: REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS
Abstract: For an agent living in a non-deterministic Markov environment (NME), what is, in theory, the fastest way of acquiring information about its statistical properties? The answer is: to design "optimal" sequences of "experimen... | [
740,
1559,
1697
] | Train |
1,704 | 2 | Title: LBG-U method for vector quantization an improvement over LBG inspired from neural networks
Abstract: Internal Report 97-01 | [
687,
745,
1157
] | Train |
1,705 | 6 | Title: Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)
Abstract: We consider learnability with membership queries in the presence of incomplete information. In the incomplete boundary query model introduced by Blum et al. [7], it is assumed that membership queries on i... | [
459,
1364,
1469,
2356
] | Test |
1,706 | 0 | Title: A Performance Model for Knowledge-based Systems
Abstract: Most techniques for verification and validation are directed at functional properties of programs. However, other properties of programs are also essential. This paper describes a model for the average computing time of a KADS knowledge-based system bas... | [
799,
1385,
1635
] | Validation |
1,707 | 0 | Title: Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*
Abstract: Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has ... | [
824,
825,
1215
] | Test |
1,708 | 6 | Title: A Simpler Look at Consistency
Abstract: One of the major goals of most early concept learners was to find hypotheses that were perfectly consistent with the training data. It was believed that this goal would indirectly achieve a high degree of predictive accuracy on a set of test data. Later research has part... | [
1333
] | Train |
1,709 | 2 | Title: NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES
Abstract: In this contribution we propose a new solution for the problem of blind separation of sources (for one dimensional signals and images) in the case that not only the waveform of sources is unknown, but also their number. For this pu... | [
59,
570,
839,
1520
] | Train |
1,710 | 2 | Title: A Non-linear Information Maximisation Algorithm that Performs Blind Separation.
Abstract: A new learning algorithm is derived which performs online stochastic gradient ascent in the mutual information between outputs and inputs of a network. In the absence of a priori knowledge about the `signal' and `noise' c... | [
576,
1450,
1656
] | Train |
1,711 | 4 | Title: Environments with Classifier Systems (Experiments on Adding Memory to XCS)
Abstract: Pier Luca Lanzi Technical Report N. 97.45 October 17 th , 1997 | [
1515,
1581
] | Validation |
1,712 | 6 | Title: An Efficient Extension to Mixture Techniques for Prediction and Decision Trees
Abstract: We present a method for maintaining mixtures of prunings of a prediction or decision tree that extends the "node-based" prunings of [Bun90, WST95, HS97] to the larger class of edge-based prunings. The method includes an ef... | [
453,
569,
1025,
1290
] | Train |
1,713 | 3 | Title: A simulation approach to convergence rates for Markov chain Monte Carlo algorithms
Abstract: Markov chain Monte Carlo (MCMC) methods, including the Gibbs sampler and the Metropolis-Hastings algorithm, are very commonly used in Bayesian statistics for sampling from complicated, high-dimensional posterior distri... | [
41,
115,
138,
416,
468,
889,
892,
1716,
1870,
1978,
1982,
1991,
1992,
2002,
2153,
2510
] | Train |
1,714 | 3 | Title: Uncertain Inferences and Uncertain Conclusions
Abstract: Uncertainty may be taken to characterize inferences, their conclusions, their premises or all three. Under some treatments of uncertainty, the inference itself is never characterized by uncertainty. We explore both the significance of uncertainty in the ... | [
1458
] | Validation |
1,715 | 1 | Title: A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction
Abstract: In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically, those techniques iteratively sampl... | [
163,
793,
1386,
1734
] | Train |
1,716 | 3 | Title: Analysis of the Gibbs sampler for a model related to James-Stein estimators
Abstract: Summary. We analyze a hierarchical Bayes model which is related to the usual empirical Bayes formulation of James-Stein estimators. We consider running a Gibbs sampler on this model. Using previous results about convergence r... | [
41,
138,
892,
1713,
1982,
1991,
2153
] | Train |
1,717 | 1 | Title: 3 Representation Issues in Neighborhood Search and Evolutionary Algorithms
Abstract: Evolutionary Algorithms are often presented as general purpose search methods. Yet, we also know that no search method is better than another over all possible problems and that in fact there is often a good deal of problem sp... | [
163,
941,
1380,
1441
] | Validation |
1,718 | 2 | Title: PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS
Abstract: We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of w... | [
28,
157,
201,
810,
1079,
1373,
1867,
2138,
2413,
2414,
2582
] | Test |
1,719 | 1 | Title: An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in
Abstract: We present a detailed analysis of the evolution of genetic programming (GP) populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth lim... | [
1216,
1257,
1911,
2175,
2261,
2363
] | Train |
1,720 | 5 | Title: Least Generalizations and Greatest Specializations of Sets of Clauses
Abstract: The main operations in Inductive Logic Programming (ILP) are generalization and specialization, which only make sense in a generality order. In ILP, the three most important generality orders are subsumption, implication and implic... | [
849
] | Test |
1,721 | 5 | Title: Machine learning in blood group determination of Danish Jersey cattle (causal probabilistic network). Dobljene mreze
Abstract: In the following paper we approach the problem with different machine learning algorithms and show that they can be compared with causal probabilistic networks in terms of performance an... | [
1569
] | Validation |
1,722 | 3 | Title: BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences
Abstract: This articles discusses developments in Bayesian time series mod-elling and analysis relevant in studies of time series in the physical and engineering sciences. With illustrations and references, w... | [
99,
1619,
1723
] | Test |
1,723 | 3 | Title: Modelling and robustness issues in Bayesian time series analysis
Abstract: Some areas of recent development and current interest in time series are noted, with some discussion of Bayesian modelling efforts motivated by substantial practical problems. The areas include non-linear auto-regressive time series mod... | [
1619,
1722
] | Test |
1,724 | 2 | Title: Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics
Abstract: We present a method for the unsupervised segmentation of data streams originating from different unknown sources which alternate in time. We use an architecture consisting of competing neural networks. Memory ... | [
1079,
1492,
1508,
1538
] | Train |
1,725 | 2 | Title: DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT
Abstract: Differential learning for statistical pattern classification is described in [5]; it is based on the classification figure-of-merit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically eff... | [
921,
1265
] | Train |
1,726 | 5 | Title: Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning
Abstract: Anaplastic thyroid carcinoma is a rare but very aggressive tumor. Many factors that might influence the survival of patients have been suggested. The aim of our study was to determine which of the... | [
314,
1182,
1569,
1679,
1684
] | Train |
1,727 | 6 | Title: Machine Learning, 22(1/2/3):95-121, 1996. On the Worst-case Analysis of Temporal-difference Learning Algorithms
Abstract: We study the behavior of a family of learning algorithms based on Sutton's method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, a... | [
565,
738,
1376
] | Validation |
1,728 | 1 | Title: Dynamic Parameter Encoding for Genetic Algorithms
Abstract: The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encod... | [
129,
163,
168,
1110,
1249,
1474,
1536,
1775
] | Train |
1,729 | 1 | Title: Adapting Crossover in a Genetic Algorithm
Abstract: Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossov... | [
163,
1016,
1110
] | Train |
1,730 | 1 | Title: Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The
Abstract: images. We apply genetic programming techniques to the production of high | [
1034,
1533
] | Train |
1,731 | 3 | Title: A Gibbs Sampling Approach to Cointegration
Abstract: This paper reviews the application of Gibbs sampling to a cointegrated VAR system. Aggregate imports and import prices for Belgium are modelled using two cointegrating relations. Gibbs sampling techniques are used to estimate from a Bayesian perspective the ... | [
888
] | Test |
1,732 | 2 | Title: Improving the Performance of Radial Basis Function Networks by Learning Center Locations
Abstract: This paper reviews the application of Gibbs sampling to a cointegrated VAR system. Aggregate imports and import prices for Belgium are modelled using two cointegrating relations. Gibbs sampling techniques are use... | [
611,
853,
1493,
1644,
2225,
2423
] | Train |
1,733 | 3 | Title: Decision Analysis by Augmented Probability Simulation
Abstract: We provide a generic Monte Carlo method to find the alternative of maximum expected utility in a decision analysis. We define an artificial distribution on the product space of alternatives and states, and show that the optimal alternative is the ... | [
41,
904
] | Train |
1,734 | 1 | Title: A Stochastic Search Approach to Grammar Induction
Abstract: This paper describes a new sampling-based heuristic for tree search named SAGE and presents an analysis of its performance on the problem of grammar induction. This last work has been inspired by the Abbadingo DFA learning competition [14] which took ... | [
163,
793,
1249,
1592,
1715
] | Test |
1,735 | 0 | Title: Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework Conversational Case-Based Reasoning
Abstract: Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integration... | [
983,
1002,
1626
] | Train |
1,736 | 1 | Title: Evolving Cooperation Strategies
Abstract: The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We propose a novel approach to the construction of cooperation strategies for a group of problem solvers ... | [
1178,
1690,
1737
] | Train |
1,737 | 1 | Title: A Simulation of Adaptive Agents in a Hostile Environment
Abstract: In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment co... | [
380,
415,
1178,
1736
] | Train |
1,738 | 1 | Title: Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects
Abstract: Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life probl... | [
219,
538,
1264,
1689
] | Train |
1,739 | 6 | Title: Model Selection based on Minimum Description Length
Abstract: Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how contr... | [
641,
848
] | Train |
1,740 | 1 | Title: As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A
Abstract: It is open as to which chromosomal dimension performs best. Although higher-dimensional encodings (whether real or imaginary) can preserve more geographical gene linkages, we suspect that too high... | [
1136,
1305
] | Test |
1,741 | 4 | Title: Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
Abstract: Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due to successes in the theoretical analysis of their behavior in Markov environments. If the Markov assumption is removed... | [
492,
565,
601,
738,
1841
] | Validation |
1,742 | 3 | Title: On MCMC Sampling in Hierarchical Longitudinal Models SUMMARY
Abstract: Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistic... | [
2456
] | Validation |
1,743 | 1 | Title: Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search
Abstract: This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and li... | [
2192
] | Validation |
1,744 | 2 | Title: Simple Synchrony Networks: Learning Generalisations across Syntactic Constituents
Abstract: This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture combining a techniqu... | [
2041
] | Test |
1,745 | 1 | Title: Learning Recursive Sequences via Evolution of Machine-Language Programs
Abstract: We use directed search techniques in the space of computer programs to learn recursive sequences of positive integers. Specifically, the integer sequences of squares, x 2 ; cubes, x 3 ; factorial, x!; and Fibonacci numbers are st... | [
2175,
2641
] | Validation |
1,746 | 2 | Title: DISCRETE-TIME TRANSITIVITY AND ACCESSIBILITY: ANALYTIC SYSTEMS 1
Abstract: This paper studies the problem, and establishes the desired implication for analytic systems in several cases: (i) compact state space, (ii) under a Poisson stability condition, and (iii) in a generic sense. In addition, the paper studi... | [
1948
] | Train |
1,747 | 3 | Title: FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS
Abstract: This paper demonstrates the use of graphs as a mathematical tool for expressing independencies, and as a formal language for communicating and processing causal information for decision analysis. We show how complex information about external interventions ca... | [
419,
1324,
1326,
1527,
2160,
2434,
2559
] | Test |
1,748 | 6 | Title: A Quantum Computational Learning Algorithm
Abstract: An interesting classical result due to Jackson allows polynomial-time learning of the function class DNF using membership queries. Since in most practical learning situations access to a membership oracle is unrealistic, this paper explores the possibility t... | [
456,
2182
] | Train |
1,749 | 3 | Title: Algebraic Techniques for Efficient Inference in Bayesian Networks
Abstract: A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. These algorithms use graph-theoretic techniques to analyze and exploit network topology. In this paper, we... | [
2164
] | Validation |
1,750 | 5 | Title: Modeling Superscalar Processors via Statistical Simulation
Abstract: A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. These algorithms use graph-theoretic techniques to analyze and exploit network topology. In this paper, we examin... | [
2106
] | Test |
1,751 | 2 | Title: Global self organization of all known protein sequences reveals inherent biological signatures self organization method
Abstract: A global classification of all currently known protein sequences is performed. Every protein sequence is partitioned into segments of 50 amino acids and a dynamic-programming distance... | [
2691
] | Train |
1,752 | 0 | Title: EXPLANATORY INTERFACE IN INTERACTIVE DESIGN ENVIRONMENTS
Abstract: Explanation is an important issue in building computer-based interactive design environments in which a human designer and a knowledge system may cooperatively solve a design problem. We consider the two related problems of explaining the syste... | [
2706
] | Validation |
1,753 | 2 | Title: Object Oriented Design of a BP Neural Network Simulator and Implementation on the Connection Machine (CM-5)
Abstract: In this paper we describe the implementation of the backpropagation algorithm by means of an object oriented library (ARCH). The use of this library relieve the user from the details of a speci... | [
2268
] | Train |
1,754 | 4 | Title: The Neural Network House: An Overview
Abstract: Typical home comfort systems utilize only rudimentary forms of energy management and conservation. The most sophisticated technology in common use today is an automatic setback thermostat. Tremendous potential remains for improving the efficiency of electric and ... | [
1867,
1869
] | Validation |
1,755 | 2 | Title: Learning Controllers from Examples a motivation for searching alternative, empirical techniques for generating controllers.
Abstract: Today there is a great interest in discovering methods that allow a faster design and development of real-time control software. Control theory helps when linear controllers ha... | [
611,
2432
] | Validation |
1,756 | 1 | Title: Soft Computing: the Convergence of Emerging Reasoning Technologies
Abstract: The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies p... | [
168,
745,
1663,
2603,
2613
] | Train |
1,757 | 3 | Title: A case study in dynamic belief networks: monitoring walking, fall prediction and detection
Abstract: The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each... | [
1268,
1842,
2221,
2341
] | Train |
1,758 | 4 | Title: Simultaneous Learning of Control Laws and Local Environment Representations for Intelligent Navigation Robots
Abstract: Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to ... | [
2412
] | Train |
1,759 | 3 | Title: Belief Maintenance in Bayesian Networks
Abstract: Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to identify which local environment it is in. This is done by first extra... | [
2288,
2697,
2698
] | Train |
1,760 | 2 | Title: Parallel Environments for Implementing Neural Networks
Abstract: As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is critical that these models run fast and generate results in real time. Although a number of implementations of neural networks are available on sequen... | [
427,
1879
] | Train |
1,761 | 3 | Title: An extension of Fill's exact sampling algorithm to non-monotone chains*
Abstract: We provide an extension of Fill's (1998) exact sampler algorithm. Our algorithm is similar to Fill's, however it makes no assumptions regarding stochastic monotonicity, discreteness of the state space, the existence of densities,... | [
2208
] | Train |
1,762 | 6 | Title: Improved Uniform Test Error Bounds
Abstract: We derive distribution-free uniform test error bounds that improve on VC-type bounds for validation. We show how to use knowledge of test inputs to improve the bounds. The bounds are sharp, but they require intense computation. We introduce a method to trade sharpne... | [
571,
2495,
2694
] | Train |
1,763 | 2 | Title: A Brief History of Connectionism
Abstract: Connectionist research is firmly established within the scientific community, especially within the multi-disciplinary field of cognitive science. This diversity, however, has created an environment which makes it difficult for connectionist researchers to remain awar... | [
407,
611,
639,
745,
2611
] | Train |
1,764 | 3 | Title: Some remarks on Scheiblechner's treatment of ISOP models.
Abstract: Scheiblechner (1995) proposes a probabilistic axiomatization of measurement called ISOP (isotonic ordinal probabilistic models) that replaces Rasch's (1980) specific objectivity assumptions with two interesting ordinal assumptions. Special cas... | [
1770,
1938
] | Train |
1,765 | 3 | Title: A Characterization of Monotone Unidimensional Latent Variable Models
Abstract: Scheiblechner (1995) proposes a probabilistic axiomatization of measurement called ISOP (isotonic ordinal probabilistic models) that replaces Rasch's (1980) specific objectivity assumptions with two interesting ordinal assumptions. ... | [
1770,
1938
] | Train |
1,766 | 2 | Title: Computational Models of Sensorimotor Integration Computational Maps and Motor Control.
Abstract: The sensorimotor integration system can be viewed as an observer attempting to estimate its own state and the state of the environment by integrating multiple sources of information. We describe a computational fr... | [
427,
477,
1810
] | Train |
1,767 | 4 | Title: Incremental Evolution of Complex General Behavior
Abstract: Several researchers have demonstrated how complex action sequences can be learned through neuro-evolution (i.e. evolving neural networks with genetic algorithms). However, complex general behavior such as evading predators or avoiding obstacles, which... | [
500,
2257
] | Train |
1,768 | 4 | Title: Evolving Neural Networks to Play Go
Abstract: Go is a difficult game for computers to master, and the best go programs are still weaker than the average human player. Since the traditional game playing techniques have proven inadequate, new approaches to computer go need to be studied. This paper presents a ne... | [
2257
] | Train |
1,769 | 1 | Title: Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation.
Abstract: Recent studies on a floating building block representation for the genetic algorithm (GA) suggest that there are many advantages to using the floating representation. This paper investigates the behavior of... | [
1696,
2330
] | Test |
1,770 | 2 | Title: A Survey of Theory and Methods of Invariant Item Ordering
Abstract: This work was initiated while Junker was visiting the University of Utrecht with the support of a Carnegie Mellon University Faculty Development Grant, and the generous hospitality of the Social Sciences Faculty, University of Utrecht. Additio... | [
1764,
1765,
1938
] | Train |
1,771 | 1 | Title: When Will a Genetic Algorithm Outperform Hill Climbing?
Abstract: We analyze a simple hill-climbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC ... | [
1696,
1775,
1872
] | Train |
1,772 | 2 | Title: New Inexact Parallel Variable Distribution Algorithms Editor:
Abstract: We consider the recently proposed parallel variable distribution (PVD) algorithm of Ferris and Mangasarian [4] for solving optimization problems in which the variables are distributed among p processors. Each processor has the primary res... | [
2307,
2351
] | Train |
1,773 | 2 | Title: Canonical Momenta Indicators of Financial Markets and Neocortical EEG
Abstract: A paradigm of statistical mechanics of financial markets (SMFM) is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians d... | [
2545
] | Train |
1,774 | 2 | Title: Networks of Spiking Neurons: The Third Generation of Neural Network Models
Abstract: The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular ... | [
990,
1891,
1968,
2619
] | Train |
1,775 | 1 | Title: GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON
Abstract: We compare Genetic Algorithms (GA) with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima. GA prev... | [
163,
1728,
1771,
1793,
1795,
2082,
2116
] | Train |
1,776 | 5 | Title: Extending Theory Refinement to M-of-N Rules
Abstract: In recent years, machine learning research has started addressing a problem known as theory refinement. The goal of a theory refinement learner is to modify an incomplete or incorrect rule base, representing a domain theory, to make it consistent with a set... | [
136,
1595,
2543
] | Train |
1,777 | 3 | Title: Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market
Abstract: We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of of uncertain events, formulate a sensible consensus or aggregate probability distribution... | [
1802,
2064
] | Test |
1,778 | 2 | Title: SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)
Abstract: Predictability minimization (PM | Schmidhuber, 1992) exhibits various intuitive and theoretical advantages over many other methods for unsupervised redundancy reduction. So far, however, t... | [
731,
2024
] | Test |
1,779 | 4 | Title: REINFORCEMENT LEARNING WITH SELF-MODIFYING POLICIES
Abstract: A learner's modifiable components are called its policy. An algorithm that modifies the policy is a learning algorithm. If the learning algorithm has modifiable components represented as part of the policy, then we speak of a self-modifying policy (... | [
2007
] | Train |
1,780 | 4 | Title: Machine Learning, Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement
Abstract: We study task sequences that allow for speeding up the learner's average reward intake through appropriate shifts of inductive bias (changes of the learner's policy). To ev... | [
2007
] | Validation |
1,781 | 5 | Title: Learning Singly-Recursive Relations from Small Datasets
Abstract: The inductive logic programming system LOPSTER was created to demonstrate the advantage of basing induction on logical implication rather than -subsumption. LOPSTER's sub-unification procedures allow it to induce recursive relations using a mini... | [
1819,
2663
] | Validation |
1,782 | 4 | Title: Least-Squares Temporal Difference Learning
Abstract: Submitted to NIPS-98 TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It has two major drawbacks: it makes inefficient use of data,... | [
134,
295,
565,
566,
2328
] | Validation |
1,783 | 3 | Title: Importance Sampling
Abstract: Technical Report No. 9805, Department of Statistics, University of Toronto Abstract. Simulated annealing | moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions | has traditionally been used as an inexact method of handling... | [
48,
2348
] | Train |
1,784 | 1 | Title: Genetic Programming and Redundancy
Abstract: The Genetic Programming optimization method (GP) elaborated by John Koza [ Koza, 1992 ] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an ... | [
55,
163,
380,
844,
1184,
2199,
2688,
2705
] | Train |
1,785 | 1 | Title: A DISCUSSION ON SOME DESIGN PRINCIPLES FOR EFFICIENT CROSSOVER OPERATORS FOR GRAPH COLORING PROBLEMS
Abstract: A year ago, a new metaheuristic for graph coloring problems was introduced by Costa, Hertz and Dubuis. They have shown, with computer experiments, some clear indication of the benefits of this approac... | [
2564
] | Validation |
1,786 | 6 | Title: Decision Trees: Equivalence and Propositional Operations
Abstract: For the well-known concept of decision trees as it is used for inductive inference we study the natural concept of equivalence: two decision trees are equivalent if and only if they represent the same hypothesis. We present a simple efficient a... | [
2207
] | Validation |
1,787 | 2 | Title: An integrated approach to the study of object features in visual recognition
Abstract: We propose to assess the relevance of theories of synaptic modification as models of feature extraction in human vision, by using masks derived from synaptic weight patterns to occlude parts of the stimulus images in psychop... | [
359,
2499,
2676
] | Train |
1,788 | 2 | Title: Path-integral evolution of chaos embedded in noise: Duffing neocortical analog
Abstract: A two dimensional time-dependent Duffing oscillator model of macroscopic neocortex exhibits chaos for some ranges of parameters. We embed this model in moderate noise, typical of the context presented in real neocortex, us... | [
1795,
2178,
2181
] | Train |
1,789 | 2 | Title: Pruning with generalization based weight saliencies: flOBD, flOBS
Abstract: The purpose of most architecture optimization schemes is to improve generalization. In this presentation we suggest to estimate the weight saliency as the associated change in generalization error if the weight is pruned. We detail the... | [
2469
] | Train |
1,790 | 1 | Title: Using Genetic Programming to Evolve Board Evaluation Functions
Abstract: The purpose of most architecture optimization schemes is to improve generalization. In this presentation we suggest to estimate the weight saliency as the associated change in generalization error if the weight is pruned. We detail the im... | [
22,
415,
523,
565,
2334
] | Validation |
1,791 | 4 | Title: Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function
Abstract: This paper introduces a new algorithm, Q2, for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids stro... | [
682,
1559,
1859
] | Train |
1,792 | 6 | Title: 21 Using n 2 classifier in constructive induction
Abstract: In this paper, we propose a multi-classification approach for constructive induction. The idea of an improvement of classification accuracy is based on iterative modification of input data space. This process is independently repeated for each pair of... | [
163,
430,
582,
2067
] | Test |
1,793 | 2 | Title: STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS
Abstract: This highly interdisciplinary project extends previous work in combat modeling and in control-theoretic descriptions of decision-making human factors in complex activities. A previous paper has established the first theory of the statistical mechanic... | [
1775,
1795,
2082,
2178,
2181
] | Train |
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