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: 9781849960977
- Medium: Buch
- ISBN: 978-1-84996-097-7
- Verlag: Springer
- Erscheinungstermin: 29.03.2010
- Sprache(n): Englisch
- Auflage: 2. 2010 Auflage 2010
- Serie: Advances in Computer Vision and Pattern Recognition
- Produktform: Gebunden
- Gewicht: 1900 g
- Seiten: 473
- Format (B x H x T): 164 x 242 x 38 mm
- Ausgabetyp: Kein, Unbekannt
- Vorauflage: 978-1-85233-929-6