A weighted hyper-sphere SVM - 文章详情页

作者：

Zhang, Xinfeng (Zhang, Xinfeng.) | Xu, Xiaozhao (Xu, Xiaozhao.) | Cai, Yiheng (Cai, Yiheng.) | Liu, Yaowei (Liu, Yaowei.)

收录：

EI Scopus

摘要：

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　η　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.

关键词：

Support vector machines Spheres

作者机构：

[ 1 ] [Zhang, Xinfeng]Signal and Information Processing Lab, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Xu, Xiaozhao]Signal and Information Processing Lab, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Cai, Yiheng]Signal and Information Processing Lab, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Liu, Yaowei]Signal and Information Processing Lab, Beijing University of Technology, Beijing, 100124, China

通讯作者信息：

电子邮件地址：

zxf@bjut.edu.cn

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