Normal view MARC view ISBD view

An introduction to computational learning theory / Michael J. Kearns, Umesh V. Vazirani.

By: Kearns, Michael J [author.].

Contributor(s): Vazirani, Umesh Virkumar | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].

Material type: materialTypeLabel

BookPublisher: Cambridge, Massachusetts : MIT Press, c1994Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [1994]Description: 1 PDF (xii, 207 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262276863.Subject(s): Neural networks (Computer science)

| Algorithms

| Artificial intelligence

| Machine learning

Genre/Form: Electronic books.Additional physical formats: Print version: No titleOnline resources: Abstract with links to resource Also available in print.

Contents:

The probably approximately correct learning model -- Occam's razor -- The Vapnik-Chervonenkis dimension -- Weak and strong learning -- Learning in the presence of noise -- Inherent unpredictability -- Reducibility in PAC learning -- Learning finite automata by experimentation -- Appendix: some tools for probabilistic analysis.

Summary: 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.

average rating: 0.0 (0 votes)

Holdings ( 0 )
Title notes
Comments ( 0 )
Book reviews by critics ( XXX )

No physical items for this record

Includes bibliographical references (p. [193]-203) and index.

Restricted to subscribers or individual electronic text purchasers.

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.

Central Library

An introduction to computational learning theory / Michael J. Kearns, Umesh V. Vazirani.

By: Kearns, Michael J [author.].

Contributor(s): Vazirani, Umesh Virkumar | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].