000			03884nam a2200529 i 4500
001			6267405
003			IEEE
005			20220712204655.0
006			m o d
007			cr |n|||||||||
008			151223s1994 maua ob 001 eng d
020			_z9780262111935 _qprint
020			_a9780262276863 _qebook
020			_z0585350531 _qelectronic
020			_z9780585350530 _qelectronic
020			_z0262276860 _qelectronic
035			_a(CaBNVSL)mat06267405
035			_a(IDAMS)0b000064818b43f1
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.K44 1994eb
100	1		_aKearns, Michael J., _eauthor. _922634
245	1	3	_aAn introduction to computational learning theory / _cMichael J. Kearns, Umesh V. Vazirani.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1994.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1994]
300			_a1 PDF (xii, 207 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references (p. [193]-203) and index.
505	0		_aThe 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.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aEmphasizing 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.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aNeural networks (Computer science) _93414
650		0	_aAlgorithms. _93390
650		0	_aArtificial intelligence. _93407
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
700	1		_aVazirani, Umesh Virkumar. _922635
710	2		_aIEEE Xplore (Online Service), _edistributor. _922636
710	2		_aMIT Press, _epublisher. _922637
776	0	8	_iPrint version _z9780262111935
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267405
942			_cEBK
999			_c73059 _d73059