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.
Produkteigenschaften
- Artikelnummer: 9781447125488
- Medium: Buch
- ISBN: 978-1-4471-2548-8
- Verlag: Springer
- Erscheinungstermin: 04.05.2012
- Sprache(n): Englisch
- Auflage: Softcover Nachdruck of hardcover 2. Auflage 2010
- Serie: Advances in Computer Vision and Pattern Recognition
- Produktform: Kartoniert, Paperback
- Gewicht: 739 g
- Seiten: 473
- Format (B x H x T): 155 x 235 x 27 mm
- Ausgabetyp: Kein, Unbekannt