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|---|---|---|---|---|
1,894 | 3 | Title: Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,
Abstract: Lipid Research Clinic Program 84] Lipid Research Clinic Program. The Lipid Research Clinics Coronary Primary Prevention Trial results, parts I and II. Journal of the American Medi... | [
827,
909,
1527,
2144,
2524
] | Train |
1,895 | 2 | Title: Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)
Abstract: We investigate the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and how s... | [
102,
638,
1893
] | Train |
1,896 | 2 | Title: Experiments with the Cascade-Correlation Algorithm
Abstract: Technical Report # 91-16 July 1991; Revised August 1991 | [
496,
1851,
2393
] | Test |
1,897 | 6 | Title: On Learning Visual Concepts and DNF Formulae
Abstract: We consider the problem of learning DNF formulae in the mistake-bound and the PAC models. We develop a new approach, which is called polynomial explainability, that is shown to be useful for learning some new subclasses of DNF (and CNF) formulae that were ... | [
25,
640,
732,
1003,
2146,
2182
] | Train |
1,898 | 3 | Title: Accounting for Context in Plan Recognition, with Application to Traffic Monitoring
Abstract: Typical approaches to plan recognition start from a representation of an agent's possible plans, and reason evidentially from observations of the agent's actions to assess the plausibility of the various candidates. A ... | [
278,
1268,
2108,
2140
] | Train |
1,899 | 3 | Title: Logarithmic Time Parallel Bayesian Inference
Abstract: I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worst-case time complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write parallel random-access machine) with ... | [
327,
2292
] | Test |
1,900 | 3 | Title: Learning Convex Sets of Probability from Data
Abstract: Several theories of inference and decision employ sets of probability distributions as the fundamental representation of (subjective) belief. This paper investigates a frequentist connection between empirical data and convex sets of probability distributi... | [
2492
] | Train |
1,901 | 3 | Title: Learning Convex Sets of Probability from Data
Abstract: This reproduces a report submitted to Rome Laboratory on October 27, 1994. c flCopyright 1994 by Jon Doyle. All rights reserved. Freely available via http://www.medg.lcs.mit.edu/doyle. Final Report on Rational Distributed Reason Maintenance for Abstract E... | [
1800,
2301
] | Train |
1,902 | 2 | Title: LU TP 91-25 Mass Reconstruction with a Neural Network
Abstract: A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W ! q q, where W -bosons are produced in pp reactions at SPS collider energies. The... | [
1884,
1885,
1886,
1887
] | Train |
1,903 | 2 | Title: DNA: A New ASOCS Model With Improved Implementation Potential
Abstract: A new class of highspeed, self-adaptive, massively parallel computing models called ASOCS (Adaptive Self-Organizing Concurrent Systems) has been proposed. Current analysis suggests that there may be problems implementing ASOCS models in VL... | [
2612
] | Train |
1,904 | 0 | Title: Using Case-Based Reasoning for Mobile Robot Navigation
Abstract: This paper presents an approach to mobile robot path planning using case-based reasoning together with map-based path planning. The map-based path planner is used to seed the case-base with innovative solutions. The casebase stores the paths and ... | [
643,
2556
] | Test |
1,905 | 1 | Title: Determining Successful Negotiation Strategies: An Evolutionary Approach
Abstract: To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the ... | [
55,
163,
1834
] | Test |
1,906 | 3 | Title: Bayesian Estimation and Model Choice in Item Response Models
Abstract: To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative su... | [
2421
] | Train |
1,907 | 3 | Title: Toward Rational Planning and Replanning Rational Reason Maintenance, Reasoning Economies, and Qualitative Preferences formal notions
Abstract: Efficiency dictates that plans for large-scale distributed activities be revised incrementally, with parts of plans being revised only if the expected utility of identify... | [
1800,
1995,
2301
] | Train |
1,908 | 3 | Title: Induction of Selective Bayesian Classifiers
Abstract: In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that car... | [
442,
1582,
1647,
1838,
1893,
2514,
2561
] | Test |
1,909 | 3 | Title: A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers
Abstract: In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classifier selects a subset of attributes that maximizes predictive accuracy prior to the network learn... | [
632,
1545,
1582,
2677
] | Validation |
1,910 | 3 | Title: Minimax Estimation via Wavelet Shrinkage a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,
Abstract: We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets we develop a nonlinear method which works in the wavelet do... | [
705,
1668,
2081,
2159,
2242,
2366,
2375,
2416,
2488,
2506,
2575,
2661
] | Test |
1,911 | 1 | Title: Genetic Programming and Data Structures
Abstract: We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients. The ... | [
290,
860,
1098,
1719,
2087,
2206
] | Validation |
1,912 | 2 | Title: Theory of Correlations in Stochastic Neural Networks
Abstract: One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however the interpretation of the observed correlations is difficult, since the correlation b... | [
304,
1932
] | Train |
1,913 | 3 | Title: A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability 1
Abstract: Technical Report no. 297 Department of Statistics University of Washington 1 Sonia Petrone is Assistant Professor, Universita di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi, I-2... | [
416,
1803
] | Test |
1,914 | 2 | Title: Local Feedforward Networks
Abstract: Although feedforward neural networks are well suited to function approximation, in some applications networks experience problems when learning a desired function. One problem is interference which occurs when learning in one area of the input space causes unlearning in ano... | [
427,
2176
] | Train |
1,915 | 2 | Title: A Mixture of Experts Model Exhibiting Prosopagnosia
Abstract: A considerable body of evidence from prosopagnosia, a deficit in face recognition dissociable from nonface object recognition, indicates that the visual system devotes a specialized functional area to mechanisms appropriate for face processing. We p... | [
1981,
2497
] | Test |
1,916 | 2 | Title: Modeling Cortical Plasticity Based on Adapting Lateral Interaction
Abstract: A neural network model called LISSOM for the cooperative self-organization of afferent and lateral connections in cortical maps is applied to modeling cortical plasticity. After self-organization, the LISSOM maps are in a dynamic equi... | [
2400
] | Train |
1,917 | 1 | Title: Go and Genetic Programming Playing Go with Filter Functions
Abstract: A neural network model called LISSOM for the cooperative self-organization of afferent and lateral connections in cortical maps is applied to modeling cortical plasticity. After self-organization, the LISSOM maps are in a dynamic equilibrium... | [
1796,
2334
] | Train |
1,918 | 6 | Title: Stochastic Logic Programs
Abstract: One way to represent a machine learning algorithm's bias over the hypothesis and instance space is as a pair of probability distributions. This approach has been taken both within Bayesian learning schemes and the framework of U-learnability. However, it is not obvious how a... | [
1290,
2329
] | Train |
1,919 | 5 | Title: Inductive Constraint Logic and the Mutagenesis Problem
Abstract: A novel approach to learning first order logic formulae from positive and negative examples is incorporated in a system named ICL (Inductive Constraint Logic). In ICL, examples are viewed as interpretations which are true or false for the target ... | [
638,
1007,
2426,
2431
] | Train |
1,920 | 2 | Title: On the Probability of Chaos in Large Dynamical Systems: A Monte Carlo Study
Abstract: In this paper we report the result of a Monte Carlo study on the probability of chaos in large dynamical systems. We use neural networks as the basis functions for the system dynamics and choose parameter values for the netwo... | [
1874
] | Test |
1,921 | 1 | Title: Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming
Abstract: The design (synthesis) of analog electrical circuits starts with a high-level statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog ci... | [
523,
1408,
1931,
2402
] | Train |
1,922 | 3 | Title: Maximum Likelihood and Covariant Algorithms for Independent Component Analysis somewhat more biologically plausible, involving no
Abstract: Bell and Sejnowski (1995) have derived a blind signal processing algorithm for a non-linear feedforward network from an information maximization viewpoint. This paper first ... | [
570,
576,
2026
] | Train |
1,923 | 2 | Title: EM Algorithms for PCA and SPCA
Abstract: I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. ... | [
71,
1928,
2114,
2227
] | Train |
1,924 | 6 | Title: Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions
Abstract: We present new algorithms for parameter estimation of HMMs. By adapting a framework used for supervised learning, we construct iterative algorithms that maximize the likelihood of the observations while also attemptin... | [
345,
2040,
2327
] | Test |
1,925 | 1 | Title: Boolean Functions Fitness Spaces
Abstract: We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity functions. We us enumeration, uniform Monte-Carlo random sampling and sampling random full trees.... | [
2133,
2206,
2392
] | Test |
1,926 | 3 | Title: Analysis of a Non-Reversible Markov Chain Sampler
Abstract: Technical Report BU-1385-M, Biometrics Unit, Cornell University Abstract We analyse the convergence to stationarity of a simple non-reversible Markov chain that serves as a model for several non-reversible Markov chain sampling methods that are used i... | [
748,
1941
] | Validation |
1,927 | 2 | Title: A Neural Architecture for Content as well as Address-Based Storage and Recall: Theory and Applications
Abstract: Technical Report BU-1385-M, Biometrics Unit, Cornell University Abstract We analyse the convergence to stationarity of a simple non-reversible Markov chain that serves as a model for several non-rev... | [
1846,
1847,
2537
] | Validation |
1,928 | 2 | Title: Mixtures of Probabilistic Principal Component Analysers
Abstract: Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alte... | [
74,
667,
1923,
2114,
2124,
2570
] | Train |
1,929 | 2 | Title: Geometry of Early Stopping in Linear Networks
Abstract: | [
2349
] | Train |
1,930 | 1 | Title: A NEW METHODOLOGY FOR REDUCING BRITTLENESS IN GENETIC PROGRAMMING optimized maneuvers for an extended two-dimensional
Abstract: programs were independently evolved using fixed and randomly-generated fitness cases. These programs were subsequently tested against a large, representative fixed population of pursuer... | [
2512
] | Test |
1,931 | 1 | Title: AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING
Abstract: This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process starts w... | [
523,
1408,
1921,
2277,
2402,
2624
] | Train |
1,932 | 2 | Title: Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization
Abstract: Computational models of neural map formation can be considered on at least three different levels of abstraction: detailed models including neural activity dynamics, weight dynamics that abstra... | [
18,
576,
745,
1912,
2024
] | Validation |
1,933 | 3 | Title: Continuous sigmoidal belief networks trained using slice sampling
Abstract: Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean Gaussian noise to its input before passing it through a s... | [
36,
748,
2660
] | Test |
1,934 | 3 | Title: Sequential Update of Bayesian Network Structure
Abstract: There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data... | [
76,
423,
558,
1816,
2463
] | Train |
1,935 | 2 | Title: Observations on Cortical Mechanisms for Object Recognition and Learning
Abstract: This paper sketches several aspects of a hypothetical cortical architecture for visual object recognition, based on a recent computational model. The scheme relies on modules for learning from examples, such as Hyperbf-like netwo... | [
611,
2340,
2499
] | Train |
1,936 | 1 | Title: COLLECTIVE ADAPTATION: THE SHARING OF BUILDING BLOCKS
Abstract: This paper sketches several aspects of a hypothetical cortical architecture for visual object recognition, based on a recent computational model. The scheme relies on modules for learning from examples, such as Hyperbf-like networks, as its basic ... | [
1943
] | Train |
1,937 | 3 | Title: Using Qualitative Relationships for Bounding Probability Distributions
Abstract: We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abst... | [
107,
389,
623,
1064,
2293
] | Validation |
1,938 | 3 | Title: Latent and manifest monotonicity in item response models
Abstract: We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operat... | [
1764,
1765,
1770
] | Test |
1,939 | 2 | Title: Serial and Parallel Multicategory Discrimination
Abstract: A parallel algorithm is proposed for a fundamental problem of machine learning, that of mul-ticategory discrimination. The algorithm is based on minimizing an error function associated with a set of highly structured linear inequalities. These inequali... | [
2307
] | Train |
1,940 | 1 | Title: A Comparison of Crossover and Mutation in Genetic Programming
Abstract: This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains... | [
860,
2175,
2220,
2250
] | Test |
1,941 | 3 | Title: SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION
Abstract: Markov chain Monte Carlo methods such as Gibbs sampling and simple forms of the Metropolis algorithm typically move about the distribution being sampled via a random walk. For the complex, high-dimensional distributions... | [
748,
1926
] | Test |
1,942 | 3 | Title: On Functional Relation between Recognition Error and Class-Selective Reject
Abstract: This report reviews various optimum decision rules for pattern recognition, namely, Bayes rule, Chow's rule (optimum error-reject tradeoff), and a recently proposed class-selective rejection rule. The latter provides an optim... | [
2573
] | Validation |
1,943 | 1 | Title: Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem
Abstract: We utilize collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning of partial solutions will be ineffective. Each weak heuristic... | [
163,
1696,
1936
] | Train |
1,944 | 5 | Title: Knowledge Acquisition with a Knowledge-Intensive Machine Learning System
Abstract: In this paper, we investigate the integration of knowledge acquisition and machine learning techniques. We argue that existing machine learning techniques can be made more useful as knowledge acquisition tools by allowing the ex... | [
1259,
2091
] | Train |
1,945 | 3 | Title: Defining Relative Likelihood in Partially-Ordered Preferential Structures
Abstract: Starting with a likelihood or preference order on worlds, we extend it to a likelihood ordering on sets of worlds in a natural way, and examine the resulting logic. Lewis earlier considered such a notion of relative likelihood ... | [
342,
729,
1993
] | Train |
1,946 | 1 | Title: Plateaus and Plateau Search in Boolean Satisfiability Problems: When to Give Up Searching and Start Again
Abstract: We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satisfiability problems. In these experiments it was freque... | [
1030,
2516
] | Train |
1,947 | 2 | Title: Rapid Quality Estimation of Neural Network Input Representations
Abstract: The choice of an input representation for a neural network can have a profound impact on its accuracy in classifying novel instances. However, neural networks are typically computationally expensive to train, making it difficult to test... | [
2557
] | Train |
1,948 | 2 | Title: Rapid Quality Estimation of Neural Network Input Representations
Abstract: FURTHER RESULTS ON CONTROLLABILITY PROPERTIES OF DISCRETE-TIME NONLINEAR SYSTEMS fl ABSTRACT Controllability questions for discrete-time nonlinear systems are addressed in this paper. In particular, we continue the search for conditions... | [
1746
] | Train |
1,949 | 2 | Title: Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, A Globally Convergent Inexact Newton Method for
Abstract: We propose an algorithm for solving systems of monotone equations which combines Newton, proximal point, and projection methodologies. An important property of the algorithm is ... | [
1960
] | Train |
1,950 | 1 | Title: Cultural Transmission of Information in Genetic Programming
Abstract: This paper shows how the performance of a genetic programming system can be improved through the addition of mechanisms for non-genetic transmission of information between individuals (culture). Teller has previously shown how genetic progra... | [
2220,
2226
] | Train |
1,951 | 0 | Title: What Kind of Adaptation do CBR Systems Need?: A Review of Current Practice
Abstract: This paper reviews a large number of CBR systems to determine when and what sort of adaptation is currently used. Three taxonomies are proposed: an adaptation-relevant taxonomy of CBR systems, a taxonomy of the tasks performed... | [
2303
] | Train |
1,952 | 2 | Title: Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms
Abstract: Constructive learning algorithms offer an approach to incremental construction of near-minimal artificial neural networks for pattern classification. Examples of such algorithms include Tower, Pyramid, Upstar... | [
503,
2029,
2393,
2396
] | Train |
1,953 | 2 | Title: that fits the asymptotics of the problem. References
Abstract: 1] D. Aldous and P. Shields. A diffusion limit for a class of randomly growing binary trees. Probability Theory, 79:509-542, 1988. [2] R. Breathnach, C. Benoist, K. O'Hare, F. Gannon, and P. Chambon. Ovalbumin gene: Evidence for leader sequence in ... | [
2107
] | Train |
1,954 | 4 | Title: TD Models: Modeling the World at a Mixture of Time Scales
Abstract: Temporal-difference (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also to predict states, i.e., to learn a model of the world's dynamics. We present theory and algorithms for intermix... | [
98,
321,
978,
2150,
2183,
2222
] | Test |
1,955 | 5 | Title: Abstract
Abstract: This paper is a scientific comparison of two code generation techniques with identical goals generation of the best possible software pipelined code for computers with instruction level parallelism. Both are variants of modulo scheduling, a framework for generation of software pipelines pion... | [
2149,
2190,
2194
] | Train |
1,956 | 5 | Title: Instructions
Abstract: Paper and BibTeX entry are available at http://www.complang.tuwien.ac.at/papers/. This paper was published in: Compiler Construction (CC '94), Springer LNCS 786, 1994, pages 158-171 Delayed Exceptions | Speculative Execution of Abstract. Superscalar processors, which execute basic blocks... | [
735,
2527,
2649
] | Test |
1,957 | 4 | Title: AVERAGED REWARD REINFORCEMENT LEARNING APPLIED TO FUZZY RULE TUNING
Abstract: Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learning method, which only requires information on the success or failure of the control application. The tuning process a... | [
565,
2536
] | Train |
1,958 | 1 | Title: Automatic Generation of Adaptive Programs Automatic Generation of Adaptive Programs. In From Animals to Animats
Abstract: Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learning method, which only requires information on the success or failure of the... | [
2220,
2226
] | Train |
1,959 | 1 | Title: Evolution-based Discovery of Hierarchical Behaviors
Abstract: Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate m... | [
120,
177,
2259
] | Train |
1,960 | 2 | Title: Journal of Convex Analysis (accepted for publication) A HYBRID PROJECTION-PROXIMAL POINT ALGORITHM
Abstract: We propose a modification of the classical proximal point algorithm for finding zeroes of a maximal monotone operator in a Hilbert space. In particular, an approximate proximal point iteration is used t... | [
1949
] | Validation |
1,961 | 5 | Title: Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers
Abstract: We present Resource Spackling, a framework for integrating register allocation and instruction scheduling that is based on a Measure and Reduce paradigm. The technique measures the resource requirements... | [
2100,
2527
] | Train |
1,962 | 6 | Title: Learning Distributions from Random Walks
Abstract: We introduce a new model of distributions generated by random walks on graphs. This model suggests a variety of learning problems, using the definitions and models of distribution learning defined in [6]. Our framework is general enough to model previously stu... | [
574,
1827,
2509
] | Train |
1,963 | 5 | Title: Learning Problem-Oriented Decision Structures from Decision Rules: The AQDT-2 System
Abstract: We introduce a new model of distributions generated by random walks on graphs. This model suggests a variety of learning problems, using the definitions and models of distribution learning defined in [6]. Our framewo... | [
286,
378,
2195
] | Test |
1,964 | 6 | Title: Constructing Nominal Xof-N Attributes
Abstract: Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of Xof-N representations. An Xof-N is a set containing one or ... | [
102,
1595,
1644,
1862,
1863,
2675
] | Validation |
1,965 | 1 | Title: Constructing Nominal Xof-N Attributes
Abstract: Co-evolution of Pursuit and Evasion II: Simulation Methods and Results fl Abstract In a previous SAB paper [10], we presented the scientific rationale for simulating the coevolution of pursuit and evasion strategies. Here, we present an overview of our simulation... | [
712,
757,
2089
] | Train |
1,966 | 2 | Title: LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997
Abstract: Learning to store information over extended time intervals via recurrent backpropagation takes a very long time, mostly due to insufficient, decaying error back flow. We briefly review Hochreiter's 1991 analysis of this problem, then addr... | [
1825
] | Test |
1,967 | 6 | Title: Separability is a Learner's Best Friend
Abstract: Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert's analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows h... | [
695,
2346
] | Test |
1,968 | 2 | Title: A Delay-Line Based Motion Detection Chip
Abstract: Inspired by a visual motion detection model for the rabbit retina and by a computational architecture used for early audition in the barn owl, we have designed a chip that employs a correlation model to report the one-dimensional field motion of a scene in rea... | [
527,
1774,
2619
] | Train |
1,969 | 4 | Title: Generalization and scaling in reinforcement learning
Abstract: In associative reinforcement learning, an environment generates input vectors, a learning system generates possible output vectors, and a reinforcement function computes feedback signals from the input-output pairs. The task is to discover and reme... | [
2051,
2200,
2309,
2363
] | Train |
1,970 | 2 | Title: The Canonical Distortion Measure in Feature Space and 1-NN Classification
Abstract: We prove that the Canonical Distortion Measure (CDM) [2, 3] is the optimal distance measure to use for 1 nearest-neighbour (1-NN) classification, and show that it reduces to squared Euclidean distance in feature space for funct... | [
2486
] | Test |
1,971 | 1 | Title: Voting for Schemata
Abstract: The schema theorem states that implicit parallel search is behind the power of the genetic algorithm. We contend that chromosomes can vote, proportionate to their fitness, for candidate schemata. We maintain a population of binary strings and ternary schemata. The string populatio... | [
163,
995,
1696,
2211
] | Test |
1,972 | 3 | Title: ON MCMC METHODS IN BAYESIAN REGRESSION ANALYSIS AND MODEL SELECTION
Abstract: The objective of statistical data analysis is not only to describe the behaviour of a system, but also to propose, construct (and then to check) a model of observed processes. Bayesian methodology offers one of possible approaches to... | [
2620
] | Train |
1,973 | 1 | Title: ON MCMC METHODS IN BAYESIAN REGRESSION ANALYSIS AND MODEL SELECTION
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. Taylor, J.D. Farmer, S. Ra... | [
129,
247,
2451
] | Test |
1,974 | 2 | Title: Data Mining for Association Rules with Unsupervised Neural Networks
Abstract: results for Gaussian mixture models and factor analysis are discussed. | [
36,
667,
2227
] | Train |
1,975 | 4 | Title: Associative Reinforcement Learning: Functions in k-DNF
Abstract: An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often... | [
427,
565,
2408,
2655,
2689
] | Test |
1,976 | 0 | Title: Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets
Abstract: This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learn... | [
1854
] | Train |
1,977 | 3 | Title: A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms
Abstract: This note considers positive recurrent Markov chains where the probability of remaining in the current state is arbitrarily close to 1. Specifically, conditions are given which ensure the non-existence of central limit the... | [
2153,
2219,
2318,
2510
] | Train |
1,978 | 3 | Title: Two convergence properties of hybrid samplers
Abstract: This note considers positive recurrent Markov chains where the probability of remaining in the current state is arbitrarily close to 1. Specifically, conditions are given which ensure the non-existence of central limit theorems for ergodic averages of fun... | [
1713,
2510
] | Test |
1,979 | 4 | Title: ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION difference between time and space is that you can't reuse time.
Abstract: A reinforcement learning system with limited computational resources interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative reward, to be obtained throu... | [
68,
1844,
1845
] | Train |
1,980 | 1 | Title: An Overview of Evolutionary Computation
Abstract: Evolutionary computation uses computational models of evolution - ary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several ev... | [
262,
758,
2202,
2457
] | Train |
1,981 | 2 | Title: Task and Spatial Frequency Effects on Face Specialization
Abstract: There is strong evidence that face processing is localized in the brain. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of c... | [
1915,
2491
] | Train |
1,982 | 3 | Title: Theoretical rates of convergence for Markov chain Monte Carlo
Abstract: We present a general method for proving rigorous, a priori bounds on the number of iterations required to achieve convergence of Markov chain Monte Carlo. We describe bounds for specific models of the Gibbs sampler, which have been obtaine... | [
41,
1713,
1716,
2153
] | Train |
1,983 | 3 | Title: Correlated Action Effects in Decision Theoretic Regression
Abstract: Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, struct... | [
2078
] | Train |
1,984 | 1 | Title: Better Trained Ants
Abstract: The problem of programming an artificial ant to follow the Santa Fe trail has been repeatedly used as a benchmark problem. Recently we have shown performance of several techniques is not much better than the best performance obtainable using uniform random search. We suggested tha... | [
2206,
2379
] | Train |
1,985 | 1 | Title: ABSTRACT
Abstract: In general, the machine learning process can be accelerated through the use of heuristic knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP... | [
995,
1231,
2065
] | Train |
1,986 | 6 | Title: BOOSTING AND NAIVE BAYESIAN LEARNING
Abstract: Although so-called naive Bayesian classification makes the unrealistic assumption that the values of the attributes of an example are independent given the class of the example, this learning method is remarkably successful in practice, and no uniformly better lea... | [
70,
569,
1329,
2338,
2462
] | Validation |
1,987 | 0 | Title: Improving Minority Class Prediction Using Case-Specific Feature Weights
Abstract: This paper addresses the problem of handling skewed class distributions within the case-based learning (CBL) framework. We first present as a baseline an information-gain-weighted CBL algorithm and apply it to three data sets fro... | [
2607
] | Test |
1,988 | 3 | Title: Trellis-Constrained Codes
Abstract: We introduce a class of iteratively decodable trellis-constrained codes as a generalization of turbocodes, low-density parity-check codes, serially-concatenated convolutional codes, and product codes. In a trellis-constrained code, multiple trellises interact to define the a... | [
2401
] | Train |
1,989 | 0 | Title: Participating in Instructional Dialogues: Finding and Exploiting Relevant Prior Explanations
Abstract: In this paper we present our research on identifying and modeling the strategies that human tutors use for integrating previous explanations into current explanations. We have used this work to develop a comp... | [
1882
] | Train |
1,990 | 2 | Title: A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING
Abstract: The RTRL algorithm for fully recurrent continually running networks (Robinson and Fallside, 1987)(Williams and Zipser, 1989) requires O(n 4 ) computations per time step, where n is the number of non-in... | [
121,
201,
233,
2093
] | Validation |
1,991 | 3 | Title: APPLICATIONS OF CHEEGER'S CONSTANT TO THE CONVERGENCE RATE OF MARKOV CHAINS ON R n
Abstract: Quantitative geometric rates of convergence for reversible Markov chains are closely related to the Cheeger's constant, which is hard to calculate for general state spaces. This article describes a geometric argument t... | [
1713,
1716,
2510
] | Validation |
1,992 | 3 | Title: Estimating L 1 Error of Kernel Estimator: Monitoring Convergence of Markov Samplers
Abstract: In many Markov chain Monte Carlo problems, the target density function is known up to a normalization constant. In this paper, we take advantage of this knowledge to facilitate the convergence diagnostic of a Markov s... | [
1713,
2153
] | Validation |
1,993 | 3 | Title: Plausibility Measures and Default Reasoning
Abstract: We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We focus on on... | [
276,
342,
729,
1945,
2546
] | Test |
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