Support Vector Machines for Pattern Classification

Description

pic info: 2007-03-23/e4_orig
Support Vector Machines for Pattern Classification
by Shigeo Abe, (Advances in Pattern Recognition)
Springer | ISBN: 1852339292 | 2005 Year | 343 Pages | PDF | 2 Mb
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. This book 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.

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.

This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.


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1. Ebooks list page : 997
2. Support Vector Machines for Pattern Classification
3. Support Vector Machines for Pattern
4. Support Vector Machines
5. An Introduction to Support Vector Machines and Other Kernel
6. Rule Extraction from Support Vector Machines
7. Support Vector Machines: Theory and Applications
8. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
9. Support Vector Machines for Antenna Array Processing and Electromagnetics
10. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
11. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
12. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
13. An Introduction to Support Vector Machines and Other Kernel based Learning Methods
14. An Introduction to Support Vector Machines and Other Kernel based Learning Methods
15. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
16. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods