Support Vector Machines for Pattern Classification

Shigeo Abe

$309.95 $247.97

Paperback

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English

Springer London Ltd
04 May 2012

Automatic control engineering; Desktop publishing; Pattern recognition; Computer vision

Series: Advances in Computer Vision and Pattern Recognition

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.

By: Shigeo Abe
Imprint: Springer London Ltd
Country of Publication: United Kingdom
Edition: Softcover reprint of hardcover 2nd ed. 2010
Dimensions: Height: 235mm, Width: 155mm, Spine: 25mm
Weight: 747g
ISBN: 9781447125488
ISBN 10: 1447125487
Series: Advances in Computer Vision and Pattern Recognition
Pages: 473
Publication Date: 04 May 2012
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Reviews for Support Vector Machines for Pattern Classification

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)