A hyper-sphere SVM introduced the margin
Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier's generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.