Support Vector Machines for Pattern Classification 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