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An introduction to computational learning theory

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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.OCLC-licensed vendor bibliographic record.

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Detail Information

Series Title

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Call Number

005 KEA i

Publisher

Cambridge, Mass. : : The MIT Press., 1994

Collation

1 online resource (xii, 207 pages) :illustrations

Language

English

ISBN/ISSN

0585350531

Classification

005

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

NEURAL NETWORKS (COMPUTER SCIENCE)
Artificial intelligence.
Machine learning.
Algorithms.

Specific Detail Info

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Statement of Responsibility

Michael J. Kearns, Umesh V. Vazirani

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Source

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Validator

maya

Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/3897.001.0001

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