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. 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: 978-1-4471-2548-8
- Editorial: Springer
- Encuadernacion: Rústica
- Fecha Publicación: 04/05/2012
- Nº Volúmenes: 1
- Idioma: Inglés