| Description |
xiv, 343 pages : illustrations ; 25 cm |
| Series |
Advances in pattern recognition |
|
Advances in pattern recognition.
|
| Contents |
1. Introduction -- 2. Two-class support vector machines -- 3. Multiclass support vector machines -- 4. Variants of support vector machines -- 5. Training methods -- 6. Feature selection and extraction -- 7. Clustering -- 8. Kernel-based methods -- 9. Maximum-margin multilayer neural networks -- 10. Maximum-margin fuzzy classifiers -- 11. Function approximation -- A. Conventional classifiers -- B. Matrices -- C. Quadratic programming -- D. Positive semidefinite kernels and reproducing kernel Hilbert space |
| Summary |
"Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. Support Vector Machines for Pattern Classification provides a unique perspective of the state-of-the-art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects of Support Vector Machines." "The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness." |
|
"Support Vector Machines for Pattern Classification supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers and students in academia and industry."--BOOK JACKET |
| Bibliography |
Includes bibliographical references (pages [319]-337) and index |
| Subject |
Text processing (Computer science)
|
|
Pattern recognition systems.
|
|
Machine learning.
|
| LC no. |
2005040265 |
| ISBN |
1852339292 alkaline paper |
|
