21 September 2001 Feature analysis: support vector machine approaches
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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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yan Shi, Tianxu Zhang, "Feature analysis: support vector machine approaches", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441458; https://doi.org/10.1117/12.441458
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