Support Vector Machines for Pattern Classification
Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their var...
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| Format: | Electronic eBook |
| Language: | English |
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London :
Springer London,
2010.
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| Series: | Advances in Pattern Recognition,
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| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Two-Class Support Vector Machines
- Multiclass Support Vector Machines
- Variants of Support Vector Machines
- Training Methods
- Kernel-Based Methods Kernel@Kernel-based method
- Feature Selection and Extraction
- Clustering
- Maximum-Margin Multilayer Neural Networks
- Maximum-Margin Fuzzy Classifiers
- Function Approximation.
