Preface; Acknowledgments; Symbols; 1 Introduction; 2 Two-Class Support Vector Machines; 3 Multiclass Support Vector Machines; 4 Variants of Support Vector Machines; 5 Training Methods; 6 Kernel-Based Methods; 7 Feature Selection and Extraction; 8 Clustering; 9 Maximum-Margin Multilayer Neural Networks; 10 Maximum-Margin Fuzzy Classifiers; 11 Function Approximation; A Conventional Classifiers; B Matrices; C Quadratic Programming; D Positive Semidefinite Kernels and Reproducing Kernel Hilbert Space; Index
Summary
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, empir
