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 Teleneurology by Internet and Telephone by Shigeo Abe
Cover of the book Control of Solar Energy Systems by Shigeo Abe
Cover of the book The Radiotherapy of Malignant Disease by Shigeo Abe
Cover of the book Clinical Trials in Rheumatology by Shigeo Abe
Cover of the book Clinical Echocardiography by Shigeo Abe
Cover of the book Electronic Value Exchange by Shigeo Abe
Cover of the book Surgical Treatment of Anal Incontinence by Shigeo Abe
Cover of the book Leadership in Healthcare by Shigeo Abe
Cover of the book Physiological Assessment of Coronary Stenoses and the Microcirculation by Shigeo Abe
Cover of the book Composite Materials by Shigeo Abe
Cover of the book Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods by Shigeo Abe
Cover of the book Creative Engineering Design Assessment by Shigeo Abe
Cover of the book Thriving Systems Theory and Metaphor-Driven Modeling by Shigeo Abe
Cover of the book Creativity and Rationale by Shigeo Abe
Cover of the book From Linear Operators to Computational Biology 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