000			04056nam a22003735i 4500
001			291092
003			MX-SnUAN
005			20170705134222.0
007			cr nn 008mamaa
008			150903s2010 xxk| o |||| 0|eng d
020			_a9781849960984 _99781849960984
024	7		_a10.1007/9781849960984 _2doi
035			_avtls000344625
039		9	_a201509030424 _bVLOAD _c201405050310 _dVLOAD _y201402061302 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ337.5
100	1		_aAbe, Shigeo. _eautor _9322207
245	1	0	_aSupport Vector Machines for Pattern Classification / _cby Shigeo Abe.
264		1	_aLondon : _bSpringer London, _c2010.
300			_axx, 473 páginas 228 ilustraciones, 114 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvances in Pattern Recognition, _x2191-6586
500			_aSpringer eBooks
505	0		_aTwo-Class Support Vector Machines -- Multiclass Support Vector Machines -- Variants of Support Vector Machines -- Training Methods -- Kernel-Based Methods Kernel@Kernel-based method -- Feature Selection and Extraction -- Clustering -- Maximum-Margin Multilayer Neural Networks -- Maximum-Margin Fuzzy Classifiers -- Function Approximation.
520			_aOriginally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction methods. Providing a unique perspective on the state of the art in SVMs, with a particular focus on classification, this thoroughly updated new edition includes a more rigorous performance comparison of classifiers and regressors. In addition to presenting various useful architectures for multiclass classification and function approximation problems, the book now also investigates evaluation criteria for classifiers and regressors. Topics and Features: Clarifies the characteristics of two-class SVMs through extensive analysis Discusses kernel methods for improving the generalization ability of conventional neural networks and fuzzy systems Contains ample illustrations, examples and computer experiments to help readers understand the concepts and their usefulness Includes performance evaluation using publicly available two-class data sets, microarray sets, multiclass data sets, and regression data sets (NEW) Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation (NEW) Covers sparse SVMs, an approach to learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning (NEW) Explores incremental training based batch training and active-set training methods, together with decomposition techniques for linear programming SVMs (NEW) Provides a discussion on variable selection for support vector regressors (NEW) An essential guide on the use of SVMs in pattern classification, this comprehensive resource will be of interest to researchers and postgraduate students, as well as professional developers. Dr. Shigeo Abe is a Professor at Kobe University, Graduate School of Engineering. He is the author of the Springer titles Neural Networks and Fuzzy Systems and Pattern Classification: Neuro-fuzzy Methods and Their Comparison.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781849960977
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84996-098-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c291092 _d291092