Support Vector Machines for Pattern Classification
Abe, Shigeo
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Format: Book - Hardcover
Released: Sat 6 Mar 2010
Catalogue Number: 0008709128
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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.
Details:
- Publisher: Springer-Verlag New York Inc
- Series: Advances in Pattern Recognition
- Editions: Subsequent
- Textual Format: Computer Applications
- Academic Level: Scholarly/Graduate
- Depth (m): 0.032
- Dewey: 004
- Edition: 2
- Height (m): 0.235
- Pages: 471
- Place Of Publication: Great Britain/British Isles
- Published Date: Sat 6 Mar 2010
- Weight (g): 839
- Width (m): 0.159
Availability
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| Also in the Advances in Pattern Recognition series (view all) |
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