000			02026nam a22002657a 4500
999			_c11848 _d11848
003			OSt
005			20231010025038.0
008			180313b ||||| |||| 00| 0 eng d
020			_a9780470641835
040			_beng _cUNIMY
050			_aQ325.5 _b.K85 2011
100			_aKulkarni, Sanjeev.
245			_aAn elementary introduction to statistical learning theory / _cSanjeev Kulkarni, Gilbert Harman.
260			_aHoboken, N.J. : _bWiley, _cc2011.
300			_axiv, 209 p. : _bill. ; _c24 cm.
490			_aWiley series in probability and statistics
504			_aIncludes bibliographical references and indexes.
505			_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"
650			_aMachine learning _xStatistical methods.
650			_aPattern recognition systems.
700			_aHarman, Gilbert.
830			_aWiley series in probability and statistics.
942			_2lcc _cBK