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Author: Martin Anthony
Publisher: Cambridge University Press
ISBN: 9780521599221
Category : Computers
Languages : en
Pages : 150
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Book Description
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
Author: Martin Anthony
Publisher: Cambridge University Press
ISBN: 9780521599221
Category : Computers
Languages : en
Pages : 150
Get Book Here
Book Description
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
Author: ACM Special Interest Group for Automata and Computability Theory
Publisher: Morgan Kaufmann
ISBN: 9781558601468
Category : Computers
Languages : en
Pages : 412
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Book Description
COLT '90 covers the proceedings of the Third Annual Workshop on Computational Learning Theory, sponsored by the ACM SIGACT/SIGART, University of Rochester, Rochester, New York on August 6-8, 1990. The book focuses on the processes, methodologies, principles, and approaches involved in computational learning theory. The selection first elaborates on inductive inference of minimal programs, learning switch configurations, computational complexity of approximating distributions by probabilistic automata, and a learning criterion for stochastic rules. The text then takes a look at inductive identification of pattern languages with restricted substitutions, learning ring-sum-expansions, sample complexity of PAC-learning using random and chosen examples, and some problems of learning with an Oracle. The book examines a mechanical method of successful scientific inquiry, boosting a weak learning algorithm by majority, and learning by distances. Discussions focus on the relation to PAC learnability, majority-vote game, boosting a weak learner by majority vote, and a paradigm of scientific inquiry. The selection is a dependable source of data for researchers interested in the computational learning theory.
Author: Ronald L. Rivest
Publisher: Morgan Kaufmann
ISBN:
Category : Computational learning theory
Languages : en
Pages : 404
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Book Description
Author: Paul Smolensky
Publisher: Psychology Press
ISBN: 1134772947
Category : Psychology
Languages : en
Pages : 865
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Book Description
Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.
Author:
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 468
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Book Description
Author: Yves Kodratoff
Publisher: Elsevier
ISBN: 0080510558
Category : Computers
Languages : en
Pages : 836
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Book Description
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Author: Balas K. Natarajan
Publisher: Elsevier
ISBN: 0080510531
Category : Computers
Languages : en
Pages : 228
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Book Description
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers.
Author: Y.A. Feldman
Publisher: Elsevier
ISBN: 0444599282
Category : Science
Languages : en
Pages : 505
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Book Description
Current research in artificial intelligence and computer vision presented at the Israeli Symposium are combined in this volume to present an invaluable resource for students, industry and research organizations. Papers have been contributed from researchers worldwide, showing the growing interest of the international community in the work done in Israel. The papers selected are varied, reflecting the most contemporary research trends.
Author: Koichi Furukawa
Publisher: Oxford University Press
ISBN: 9780198538677
Category : Business & Economics
Languages : en
Pages : 518
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Book Description
The Machine Intelligence series was founded in 1965 by Donald Michie and has included many of the most important developments in the field over the past decades. This volume focuses on the theme of intelligent agents and features work by a number of eminent figures in artificial intelligence, including John McCarthy, Alan Robinson, Robert Kowalski, and Mike Genesereth. Topics include representations of consciousness, SoftBots, parallel implementations of logic, machine learning, machine vision, and machine-based scientific discovery in molecular biology.
Author: Alan L. Meyrowitz
Publisher: Springer Science & Business Media
ISBN: 0585273669
Category : Computers
Languages : en
Pages : 341
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Book Description
One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made.
