Overview

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.

Full Product Details

9781849969635

Table of Contents

Reviews

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)

Author Information

Tab Content 6

Customer Reviews

Recent Reviews

Countries Available