000			04280nam a22004935i 4500
001			u372827
003			SIRSI
005			20160812084117.0
007			cr nn 008mamaa
008			100721s2010 xxk| s |||| 0|eng d
020			_a9781849960984 _9978-1-84996-098-4
040			_cMX-MeUAM
050		4	_aQ337.5
050		4	_aTK7882.P3
082	0	4	_a006.4 _223
100	1		_aAbe, Shigeo. _eauthor.
245	1	0	_aSupport Vector Machines for Pattern Classification _h[recurso electrónico] / _cby Shigeo Abe.
264		1	_aLondon : _bSpringer London, _c2010.
300			_aXX, 473p. 228 illus., 114 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Pattern Recognition, _x2191-6586
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.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650		0	_aText processing (Computer science.
650		0	_aOptical pattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aDocument Preparation and Text Processing.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aControl, Robotics, Mechatronics.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781849960977
830		0	_aAdvances in Pattern Recognition, _x2191-6586
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-84996-098-4
596			_a19
942			_cLIBRO_ELEC
999			_c200707 _d200707