Support Vector Machines for Pattern Classification

• Main Author: Abe, Shigeo
• Format: eBook
• Language: English; German
• Published: London Springer Nature 2010; Springer Verlag London Limited; Springer London, Limited; Springer London; Springer
• Edition: 2. Aufl.
• Series: Advances in Pattern Recognition
• Subjects: Apprentissage automatique; Artificial Intelligence; Computer Science; Control and Systems Theory; Control, Robotics, Mechatronics; Engineering; Machine learning; Natural Language Processing (NLP); Other branches of engineering; Pattern Recognition; Pattern recognition systems; Reconnaissance des formes (informatique); Text processing (Computer science); Computer Elektronik
• ISBN: 9781849960984; 1849960984; 9781849960977; 1849960976
• ISSN: 2191-6586
• DOI: 10.1007/978-1-84996-098-4

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. TOC: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.