Support Vector Machines for Pattern Classification

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Springer Science & Business Media, Jul 23, 2010 - Technology & Engineering - 473 pages
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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.

 

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Contents

1 Introduction
1
2 TwoClass Support Vector Machines
20
3 Multiclass Support Vector Machines
113
4 Variants of Support Vector Machines
163
5 Training Methods
227
6 KernelBased Methods
304
7 Feature Selection and Extraction
331
8 Clustering
342
9 MaximumMargin Multilayer Neural Networks
353
10 MaximumMargin Fuzzy Classifiers
367
11 Function Approximation
395
A Conventional Classifiers
443
B Matrices
447
C Quadratic Programming
455
D Positive Semidefinite Kernels and Reproducing Kernel Hilbert Space
459
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