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An Introduction to Computational Learning Theory : An Introduction to Computational Learning Theory - Michael J. Kearns

An Introduction to Computational Learning Theory

An Introduction to Computational Learning Theory

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Emphasizing issues of computational efficiency, this text introduces 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.

Preface
The Probably Approximately Correct Learning Modelp. 1
Occam's Razorp. 31
The Vapnik-Chervonenkis Dimensionp. 49
Weak and Strong Learningp. 73
Learning in the Presence of Noisep. 103
Inherent Unpredictabilityp. 123
Reducibility in PAC Learningp. 143
Learning Finite Automata by Experimentationp. 155
Appendix: Some Tools for Probabilistic Analysisp. 189
Bibliographyp. 193
Indexp. 205
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780262111935
ISBN-10: 0262111934
Series: An Introduction to Computational Learning Theory
Audience: Professional
For Ages: 18+ years old
Format: Hardcover
Language: English
Number Of Pages: 222
Published: 15th August 1994
Country of Publication: US
Dimensions (cm): 22.9 x 17.8  x 1.1
Weight (kg): 0.59