000			03028cam a2200349 i 4500
001			17534559
003			OSt
005			20220616123806.0
008			121119t20132013flua b 001 0 eng
010			_a 2012033209
020			_a9781439857922 _qhardcover
040			_aDLC _beng _cDLC _erda _dDLC _dUOC
042			_apcc
082	0	0	_a519.6 _223 _bNAI
100	1		_aDeng, Naiyang _9753 _eauthor.
245	1	0	_aSupport vector machines : _boptimization based theory, algorithms, and extensions / _cNaiyang Deng, Yingjie Tian, Chunhua Zhang.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c[2013].
264		4	_c©2013.
300			_axxvii, 335 pages : _billustrations ; _c24 cm.
336			_atext _2rdacontent _btxt
337			_aunmediated _2rdamedia _bn
338			_avolume _2rdacarrier _bnc
490	0		_aChapman & Hall/CRC data mining and knowledge discovery series
504			_aIncludes bibliographical references and index.
520			_a"Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfully applied in many fields such as text categorization, speech recognition, remote sensing image analysis, time series forecasting, information security and etc. SVMs, having their roots in Statistical Learning Theory (SLT) and optimization methods, become powerful tools to solve the problems of machine learning with finite training points and to overcome some traditional difficulties such as the "curse of dimensionality", "over-fitting" and etc. SVMs theoretical foundation and implementation techniques have been established and SVMs are gaining quick development and popularity due to their many attractive features: nice mathematical representations, geometrical explanations, good generalization abilities and promising empirical performance. Some SVM monographs, including more sophisticated ones such as Cristianini & Shawe-Taylor [39] and Scholkopf & Smola [124], have been published. We have published two books about SVMs in Science Press of China since 2004 [42, 43], which attracted widespread concerns and received favorable comments. After several years research and teaching, we decide to rewrite the books and add new research achievements. The starting point and focus of the book is optimization theory, which is different from other books on SVMs in this respect. Optimization is one of the pillars on which SVMs are built, so it makes a lot of sense to consider them from this point of view"-- _cProvided by publisher.
650		0	_aMathematical optimization. _93130
700	1		_aTian, Yingjie, _d1973- _eauthor. _9754
700	1		_aZhang, Chunhua, _d1978- _eauthor. _9755
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c217 _d217