Support Vector Machines for Pattern Classification

Nonfiction, Science & Nature, Technology, Automation, Computers, Advanced Computing, Engineering, Computer Vision
Cover of the book Support Vector Machines for Pattern Classification by Shigeo Abe, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Shigeo Abe ISBN: 9781849960984
Publisher: Springer London Publication: July 23, 2010
Imprint: Springer Language: English
Author: Shigeo Abe
ISBN: 9781849960984
Publisher: Springer London
Publication: July 23, 2010
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book Offshore Medicine by Shigeo Abe
Cover of the book Photovoltaic Sources by Shigeo Abe
Cover of the book Toxic Trauma by Shigeo Abe
Cover of the book Photophysics of Carbon Nanotubes Interfaced with Organic and Inorganic Materials by Shigeo Abe
Cover of the book Congenital Displacement of the Hip Joint by Shigeo Abe
Cover of the book Histopathology Reporting by Shigeo Abe
Cover of the book S-Variable Approach to LMI-Based Robust Control by Shigeo Abe
Cover of the book Reinventing Ourselves: Contemporary Concepts of Identity in Virtual Worlds by Shigeo Abe
Cover of the book Generalized Dermatitis in Clinical Practice by Shigeo Abe
Cover of the book Waste to Energy by Shigeo Abe
Cover of the book Clinical Cardiac Electrophysiology in Clinical Practice by Shigeo Abe
Cover of the book Computational Techniques for Structural Health Monitoring by Shigeo Abe
Cover of the book Exergy by Shigeo Abe
Cover of the book Handbook of Biometric Anti-Spoofing by Shigeo Abe
Cover of the book Global Energy Policy and Security by Shigeo Abe
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy