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An introduction to computational learning theory

Author: Michael J Kearns; Umesh Virkumar Vazirani
Publisher: Cambridge, Mass. : MIT Press, ©1994.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kearns, Michael J.
Introduction to computational learning theory.
Cambridge, Mass. : MIT Press, ©1994
(DLC) 94016588
(OCoLC)30476515
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Michael J Kearns; Umesh Virkumar Vazirani
ISBN: 0585350531 9780585350530 0262276860 9780262276863
OCLC Number: 47009798
Description: 1 online resource (xii, 207 pages) : illustrations
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.
Responsibility: Michael J. Kearns, Umesh V. Vazirani.

Abstract:

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  Read more...

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