Feature analysis: support vector machine approaches

Yan Shi; Tianxu Zhang

doi:10.1117/12.441458

21 September 2001 Feature analysis: support vector machine approaches

Yan Shi, Tianxu Zhang

Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441458
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Yan Shi and Tianxu Zhang "Feature analysis: support vector machine approaches", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441458

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