000			02054nam a2200241 a 4500
001			ASIN8120334353
003			OSt
005			20200227140230.0
008			160113s2009 xxu eng d
020			_a8120334353 (paperback)
020			_a9788120334359 (paperback)
037			_bAllied Book _cPKR 833.85
040			_cAUMC
082			_a006.31
100	1		_aSoman, K. P.
245	1	0	_aMachine learning..........
260			_aNew Delhi : _bPHI Learning, _c2009.
300			_axviii,477 p. ; _c24 cm. ( Rk #15 Sh # 03)
520			_aSupport vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. This title includes extensive coverage of Lagrangian duality and iterative methods for optimization; separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing; a chapter on latest sequential minimization algorithms and its modifications to do online learning; step-by-step method of solving the SVM based classification problem in Excel; and, kernel versions of PCA, CCA and ICA. The CD accompanying this book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software. In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.
700	1		_aLoganathan, R.
856	4	0	_3Amazon.com _uhttp://www.amazon.com/exec/obidos/ASIN/8120334353/chopaconline-20
942			_2ddc _cBK
999			_c20170 _d20170