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Proceedings Paper

Feature analysis: support vector machine approaches
Author(s): Yan Shi; Tianxu Zhang
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Paper Abstract

This paper demonstrates a novel criterion for both feature ranking and feature selection using Support Vector Machines (SVMs). The method analyses the importance of feature subset using the bound on the expected error probability of an SVM. In addition a scheme for feature ranking based on SVMs is presented. Experiments show that the proposed schemes perform well in feature ranking/selection, and risk bound based criterion is superior to some other criterions.

Paper Details

Date Published: 21 September 2001
PDF: 7 pages
Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441458
Show Author Affiliations
Yan Shi, Huazhong Univ. of Science and Technology (China)
Tianxu Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 4550:
Image Extraction, Segmentation, and Recognition

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