Support Vector Machines for Pattern Classification

von Shigeo Abe

473 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: Advances in Computer Vision and Pattern Recognition
• Themen: Technologie, Ingenieurswissenschaft, Landwirtschaft, Industrieprozesse Elektronik, Nachrichtentechnik Elektronik Regelungstechnik
• ISBN: 9781447125488
• Sprache: Englisch
• Erscheinungsdatum: 04.05.2012
• Größe: 23.5 x 15.5 cm
• Verlag: Springer London