Support Vector Machines for Pattern Classification Contributor(s): Abe, Shigeo (Author) |
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ISBN: 1447125487 ISBN-13: 9781447125488 Publisher: Springer OUR PRICE: $161.49 Product Type: Paperback - Other Formats Published: May 2012 |
Additional Information |
BISAC Categories: - Computers | Computer Vision & Pattern Recognition - Computers | Document Management - Technology & Engineering | Automation |
Dewey: 005.52 |
Series: Advances in Computer Vision and Pattern Recognition |
Physical Information: 1.1" H x 6.1" W x 9" (1.50 lbs) 473 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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. |