A Weighted Hyper-Sphere SVM
Xinfeng Zhang, Xiaozhao Xu, Yiheng Cai, Yaowei Liu · 2009
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.