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中文摘要：
超球面支撑向量机是不均衡样本分类的一种重要方法．然而，目前引入间隔的超球面支撑向量机中，当一类样本集中不存在支撑向量时，两类样本之间的间隔解是不确定的；在两类样本均存在正常支撑向量的情况下，两类样本之间的间隔为零．间隔不确定或为零在很大程度上影响分类器的推广性能．为此提出了一种广义的超球面支撑向量机算法，通过引入参数n和b，理论推导得出，n〉b，这样可以保证获得不为零的间隔解．理论分析和实验结果表明，所提供算法在具有较小经验风险的同时，可获得较好的推广性能．
英文摘要：
Hyper-sphere support vector machine is an important method for unbalanced classification which is an important issue in biomedical engineering such as tongue image classification in traditional chinese medicine. By introducing the other kind of samples, one class support vector domain description classifier is modified to the binary hyper-sphere classifier to improve its generalization performance. However, among the methods in the present references, it is proved that the solution of margin between two classes of samples is uncertain when there is no support vector in one class samples from the point of the optimal solution in this paper. Meantime, the margin between two Classes is proved to be zero when there is at least one normal support vector in each kind of samples. The generalization performance is poor if the margin is an uncertain value or it equals to zero to some extent. To improve the right classification rate of the samples, a generalized hyper-sphere SVM is proposed by introducing the parameter n and b （n）b） and the margin which is greater than zero may be acquired, which balances the volume of the hyper-sphere, margin and the experimental error. Theory analysis and experimental results show that the proposed algorithm has better generalization performance and less experimental risk.
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