基于二叉树的多类SVM在Web文本分类中的应用研究

The Application of Multi-class SVM based Binary Tree in Web Text Categorization

针对现有多分类支持向量机算法所存在的训练时间长、判别速度慢等问题,提出了一种二叉树多类支持向量机算法,该算法能够有效减少支持向量的个数,从而减少训练时间.为了验证算法的有效性,将该算法分别同l-v-r算法和l-v-1算法进行了比较,实验结果表明,提出的算法是有效可行的.

A method of binary tree multi-class support vector machine algorithm has been proposed to solve the problems of the current multi-class support vector machine algorithm,which can effectively reduce the members of support vectors and training time.Compared with l-v-r and l-v-l algorithm,experiments shows that the binary tree multi-class support vector machine algorithm is more effective and feasible.