Seetha, H and Murty, Narasimha M and Saravanan, R (2011) On improving the generalization of SVM classifier. In: 5th International Conference on Information Processing, ICIP 2011, August 5-7, 2011, Bangalore, India.
Full text not available from this repository. (Request a copy)Abstract
The generalization performance of the SVM classifier depends mainly on the VC dimension and the dimensionality of the data. By reducing the VC dimension of the SVM classifier, its generalization performance is expected to increase. In the present paper, we argue that the VC dimension of SVM classifier can be reduced by applying bootstrapping and dimensionality reduction techniques. Experimental results showed that bootstrapping the original data and bootstrapping the projected (dimensionally reduced) data improved the performance of the SVM classifier.
| Item Type: | Conference Proceedings |
|---|---|
| Publisher: | Springer-Verlag GmbH Berlin Heidelberg |
| Additional Information: | Copyright of this article belongs to Springer-Verlag GmbH Berlin Heidelberg. |
| Keywords: | Bootstrapping; LDA; Outlier Removal; PCA; Random Projection; VC Dimension |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 02 Mar 2013 05:33 |
| Last Modified: | 02 Mar 2013 05:33 |
| URI: | http://eprints.iisc.ac.in/id/eprint/45978 |
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