Non-Fiction Books:

Support Vector Machines for Pattern Classification

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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.
Release date Australia
March 29th, 2010
Author
Audience
  • Professional & Vocational
Edition
2nd ed. 2010
Illustrations
114 Illustrations, black and white; XX, 473 p. 114 illus.
Pages
473
Dimensions
156x234x26
ISBN-13
9781849960977
Product ID
6276323

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