A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, andthe effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifiersystems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques forlinear programming SVMs; Discusses variable selection for support vector regressors." A comprehensive resource for the use of Support Vector Machines in Pattern Classification Takes the unique approach of focussing on classification rather than covering the theoretical aspects of Support Vector Machines Includes application of SVMs to pattern classification, extensive discussions on multiclass support vector machines, and performance evaluation of major methods using benchmark data sets INDICE: Introduction.- Two-Class Support Vector Machines.- Multiclass Support Vector Machines.- Variants of Support Vector Machines.- Training Methods.-Kernel-Based Methods.- Feature Selection and Extraction.- Clustering.- Maximum-Margin Multilayer Neural Networks.- Maximum-Margin Fuzzy Classifiers.- Function Approximation.
- ISBN: 978-1-84996-097-7
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 473
- Fecha Publicación: 01/04/2010
- Nº Volúmenes: 1
- Idioma: Inglés