Knowledge and Information Systems
Spoken language classification using hybrid classifier combination
International Journal of Hybrid Intelligent Systems
A rough margin based support vector machine
Information Sciences: an International Journal
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Support vector machines based on weighted scatter degree
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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It is generally believed that the support vector machine (SVM) optimizes the generalization error and outperforms other learning machines. We show analytically, by concrete examples in the one dimensional case, that the SVM does improve the mean and standard deviation of the generalization error by a constant factor, compared to the worst learning machine. Our approach is in terms of the extreme value theory and both the mean and variance of the generalization errors are calculated exactly for all the cases considered. We propose a new version of the SVM , called the scaled SVM, which can further reduce the mean of the generalization error of the SVM