An Introduction to Computational Learning Theory / Edition 1

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Overview

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning;Occam's Razor, which formalizes a relationship between learning and data compression; theVapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

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Editorial Reviews

Booknews
Contributes to the search for common methods underlying efficient learning algorithms and computational impediments to learning. Topics such as Occam's razor, the Vapnik-Chervonenkis dimension and learning in the presence of noise are discussed to illuminate basic principles. For researchers in artificial intelligence and related fields. Annotation c. Book News, Inc., Portland, OR booknews.com
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Product Details

  • ISBN-13: 9780262111935
  • Publisher: MIT Press
  • Publication date: 8/15/1994
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 221
  • Sales rank: 829,051
  • Product dimensions: 7.10 (w) x 9.25 (h) x 0.65 (d)

Meet the Author

Michael J. Kearns is Professor of Computer and Information Science at the University ofPennsylvania.

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Table of Contents

Preface
1 The Probably Approximately Correct Learning Model 1
2 Occam's Razor 31
3 The Vapnik-Chervonenkis Dimension 49
4 Weak and Strong Learning 73
5 Learning in the Presence of Noise 103
6 Inherent Unpredictability 123
7 Reducibility in PAC Learning 143
8 Learning Finite Automata by Experimentation 155
9 Appendix: Some Tools for Probabilistic Analysis 189
Bibliography 193
Index 205
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