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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, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.
List of contents
Two-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.
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, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.
Additional text
From the reviews:
"This broad and deep … book is organized around the highly significant concept of pattern recognition by support vector machines (SVMs). … The book is praxis and application oriented but with strong theoretical backing and support. Many … details are presented and discussed, thereby making the SVM both an easy-to-understand learning machine and a more likable data modeling (mining) tool. Shigeo Abe has produced the book that will become the standard … . I like it and therefore highly recommend this book … ." (Vojislav Kecman, SIAM Review, Vol. 48 (2), 2006)
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From the reviews:
"This broad and deep ... book is organized around the highly significant concept of pattern recognition by support vector machines (SVMs). ... The book is praxis and application oriented but with strong theoretical backing and support. Many ... details are presented and discussed, thereby making the SVM both an easy-to-understand learning machine and a more likable data modeling (mining) tool. Shigeo Abe has produced the book that will become the standard ... . I like it and therefore highly recommend this book ... ." (Vojislav Kecman, SIAM Review, Vol. 48 (2), 2006)
