Overview
- Carefully edited volume presenting the state of the art of Support Vector Machines
- Presents theory, algorithms and applications
- Includes numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 177)
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Table of contents (20 chapters)
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Front Matter
Keywords
About this book
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.
Bibliographic Information
Book Title: Support Vector Machines: Theory and Applications
Editors: Lipo Wang
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/b95439
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Hardcover ISBN: 978-3-540-24388-5Published: 21 June 2005
Softcover ISBN: 978-3-642-06368-8Published: 17 November 2010
eBook ISBN: 978-3-540-32384-6Published: 10 May 2005
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: X, 431
Topics: Theory of Computation, Mathematical and Computational Engineering, Artificial Intelligence, Pattern Recognition