古丽娜孜, 孙铁利. 基于二叉树的多类SVM在Web文本分类中的应用研究[J]. Journal of Xinjiang University (Natural Science Edition in Chinese and English), 2011, 28(1): 100-104.DOI：
基于二叉树的多类SVM在Web文本分类中的应用研究
摘要
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.
Abstract
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.
关键词
Keywords
references
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