Support vector machines for pattern classification
Author(s)
Bibliographic Information
Support vector machines for pattern classification
（Advances in pattern recognition）
Springer, c2010
2nd ed
- : pbk.
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Note
Includes bibliographical references and index
Description and Table of Contents
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.
Table of Contents
Introduction
Two-Class Support Vector Machines
Multiclass Support Vector Machines
Variants of Support Vector Machines
Training Methods
Kernel-Based Methods
Feature Selection and Extraction
Clustering
Maximum-Margin Multilayer Neural Networks
Maximum-Margin Fuzzy Classifiers
Function Approximation.
by "Nielsen BookData"