A Weighted Hyper-Sphere SVM

Author

Zhang, Xinfeng ; Xu, Xiaozhao ; Cai, Yiheng ; Liu, Yaowei

Author_Institution

Signal & Inf. Process. Lab., Beijing Univ. of Technol., Beijing, China

Volume

3

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

574

Lastpage

577

Abstract

Generalized hyper-sphere SVM is a promising method for the pattern classification. The ratio of the support vectors from two classes of samples can not be adjusted conveniently by setting the parameters n and b in the generalized hyper-sphere SVM (GHSVM), which affects the generalization performance to some extent. A weighted hyper-sphere SVM is studied in this paper. The results shows that the margin may be obtained much more easily by weighted method rather than by adjusting the parameters n and b, which makes the classifier´s generalization performance much better than the original GHSVM.

Keywords

generalisation (artificial intelligence); pattern classification; support vector machines; generalization performance; generalized hyper-sphere SVM; pattern classification; support vectors; weighted hyper-sphere SVM; Face detection; Image analysis; Image classification; Information processing; Pattern classification; Performance analysis; Signal processing; Support vector machine classification; Support vector machines; Tongue; Generalized hyper-sphere SVM; Weigheted; generalization performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.398

Filename

5366182