An Introduction to Computational Learning Theory / Edition 1

An Introduction to Computational Learning Theory / Edition 1

by Michael J. Kearns, Umesh Vazirani
     
 

ISBN-10: 0262111934

ISBN-13: 9780262111935

Pub. Date: 08/15/1994

Publisher: MIT Press

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

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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; the
Vapnik-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.

The MIT Press

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Product Details

ISBN-13:
9780262111935
Publisher:
MIT Press
Publication date:
08/15/1994
Edition description:
New Edition
Pages:
221
Product dimensions:
7.10(w) x 9.25(h) x 0.65(d)
Age Range:
18 Years

Table of Contents

Preface
1The Probably Approximately Correct Learning Model1
2Occam's Razor31
3The Vapnik-Chervonenkis Dimension49
4Weak and Strong Learning73
5Learning in the Presence of Noise103
6Inherent Unpredictability123
7Reducibility in PAC Learning143
8Learning Finite Automata by Experimentation155
9Appendix: Some Tools for Probabilistic Analysis189
Bibliography193
Index205

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