Overview
- A comprehensive resource for the use of Support Vector Machines in Pattern Classification
- Takes the unique approach of focussing on classification rather than covering the theoretical aspects of Support Vector Machines
- Includes application of SVMs to pattern classification, extensive discussions on multiclass support vector machines, and performance evaluation of major methods using benchmark data sets
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
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.
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Keywords
Table of contents (11 chapters)
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Front Matter
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Back Matter
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)
Authors and Affiliations
Bibliographic Information
Book Title: Support Vector Machines for Pattern Classification
Authors: Shigeo Abe
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-1-84996-098-4
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2010
Hardcover ISBN: 978-1-84996-097-7Published: 29 March 2010
Softcover ISBN: 978-1-4471-2548-8Published: 04 May 2012
eBook ISBN: 978-1-84996-098-4Published: 23 July 2010
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 2
Number of Pages: XX, 473
Number of Illustrations: 114 b/w illustrations
Topics: Control and Systems Theory, Pattern Recognition, Natural Language Processing (NLP), Artificial Intelligence, Control, Robotics, Mechatronics