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

Format:

Book

Author/Creator:

Kearns, Michael J.

Contributor:

Vazirani, Umesh Virkumar.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Algorithms.

Neural networks (Computer science).

Physical Description:

xii, 207 pages : illustrations ; 24 cm

Place of Publication:

Cambridge, Mass. : MIT Press, [1994]

Summary:

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.

Notes:

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

ISBN:

0262111934

OCLC:

30476515