改进的超球支持向量机算法

Improved hyper-sphere Support Vector Machine

超球支持向量机算法用于解决多类别数据的分类问题。对超球重叠区域的数据正确分类对球结构支持向量机的分类性能至关重要。在分析这些样本点特点的基础上,提出了一种新的分类规则,使超球支持向量机算法的泛化性能高于现有的算法。实验结果表明该算法有效可行,提高了最小包围球分类器的分类精度。

Hyper-sphere support vector machines are proposed for solving muhi-class classification problem.How to correctly classify the intersections of hyper-spberes is important for sphere structure support vector machines.Based on the analysis of such data samples,this paper presents a new simple classification rule which leads to a better generalization accuracy than the existing sub-hyper-sphere SVMs.Experimental results show the method is feasible and improves the performance of the resulting minimum bounding sphere-based classifier.