Support Vector Machines for Pattern Classification / Edition 2

Support Vector Machines for Pattern Classification / Edition 2

by Shigeo Abe
     
 

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ISBN-10: 1447125487

ISBN-13: 9781447125488

Pub. Date: 05/03/2012

Publisher: Springer London

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

Overview

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.

Product Details

ISBN-13:
9781447125488
Publisher:
Springer London
Publication date:
05/03/2012
Series:
Advances in Computer Vision and Pattern Recognition Series
Edition description:
Softcover reprint of hardcover 2nd ed. 2010
Pages:
473
Product dimensions:
6.10(w) x 9.25(h) x 0.04(d)

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.

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