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

Publication Info.	Cambridge, Mass. : MIT Press, [1994]
	©1994

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Online eBook. Access restricted to current Rider University students, faculty, and staff.
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Description	1 online resource (xii, 207 pages) : illustrations
Physical Medium	polychrome
Description	text file
Bibliography	Includes bibliographical references (pages 193-203) and index.
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.
Local Note	eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America
Subject	Machine learning.
	Machine learning.
	Artificial intelligence.
	Artificial intelligence.
	Algorithms.
	Algorithms.
	Neural networks (Computer science)
	Neural networks (Computer science)
Genre/Form	Electronic books.
Added Author	Vazirani, Umesh Virkumar.
Other Form:	Print version: Kearns, Michael J. Introduction to computational learning theory. Cambridge, Mass. : MIT Press, ©1994 0262111934 (DLC) 94016588 (OCoLC)30476515
ISBN	0585350531 (electronic book)
	9780585350530 (electronic book)
	0262276860 (electronic book)
	9780262276863 (electronic book)
	0262111934
	9780262111935