000			02604cam a22004574a 4500
001			8834492
003			BD-DhUL
005			20150219124343.0
006			m d
007			cr n
008			101123s2011 njua sb 001 0 eng d
020			_a9780470641835 (cloth)
020			_a0470641835 (cloth)
020			_a9781118023433
020			_a1118023439
020			_a9781118023464
020			_a1118023463
020			_a9781118023471
020			_a1118023471
035			_a(WaSeSS)ssj0000476962
040			_aDLC _cDLC _dYDX _dCDX _dYDXCP _dOIP _dZ@L _dDEBBG _dDLC _dWaSeSS _dBD-DhUL
042			_apcc
050		4	_aQ325.5 _b.K85 2011
082	0	0	_a006.31 _222 _bKUE
084			_aST 300 _2rvk
100	1		_aKulkarni, Sanjeev.
210	1	0	_aAn elementary introduction to statistical learning theory
245	1	3	_aAn elementary introduction to statistical learning theory / _cSanjeev Kulkarni, Gilbert Harman.
260			_aHoboken, N.J. : _bWiley, _cc2011.
300			_axi, 209 p.: _bill. ; _c24 cm.
365			_aUS$ _b89.06
490	1		_aWiley series in probability and statistics
504			_aIncludes bibliographical references and indexes.
505	0		_aIntroduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting -- Bibliography.
520			_a"A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.
650		0	_aMachine learning _xStatistical methods.
650		0	_aPattern recognition systems.
700	1		_aHarman, Gilbert.
830		0	_aWiley series in probability and statistics.
942			_2ddc _cBK
999			_c1316 _d1316