中文摘要：

针对现有的支持向量机多类分类方法的不足之处,提出了一种基于超球体支持向量机的不完全二叉树多类分类算法。该算法首先采用超球体SVM算法,计算各类样本群的分布范围。再利用距离公式,计算各类样本间的距离,基于将最容易分离出来的类最先分割出来的原则,设计二叉树结构,从而提高分类精度。通过仿真实验,分析比较各种方法的性能,从而验证了该算法的有效性。

英文摘要：

On the base of current researches on multiclass classification with support vector machine,an incomplete binary tree SVM multi-class classification algorithm based on hypersphere is proposed.The algorithm adopts hypersphere SVM algorithm to calculate the distribution of each sample groups.Then,the distance formula is used to calculate the distance among the sample classes.According to the principle that the class which can be separated easiest must be split first,the algorithm designs binary tree to improve the classification accuracy.Compared with many classification methods,the effectiveness of the algorithm is verified by simulation experiments.