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Support Vector Machines for Pattern Classification

By Shigeo Abe

  • eBook Price: $149.00
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This guide on the use of SVMs in pattern classification includes a rigorous performance comparison of classifiers and regressors. The book takes the unique approach of focusing on classification rather than covering the theoretical aspects of SVMs.

Full Description

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  • ISBN13: 978-1-8499-6097-7
  • 492 Pages
  • User Level: Science
  • Publication Date: July 23, 2010
  • Available eBook Formats: PDF
Full 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

Table of Contents

  1. Introduction.
  2. Two
  3. Class Support Vector Machines.
  4. Multiclass Support Vector Machines.
  5. Variants of Support Vector Machines.
  6. Training Methods.
  7. Kernel
  8. Based Methods.
  9. Feature Selection and Extraction.
  10. Clustering.
  11. Maximum
  12. Margin Multilayer Neural Networks.
  13. Maximum
  14. Margin Fuzzy Classifiers.
  15. Function Approximation.

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