基于类间可分性DAG-SVM的文本分类  

Text classification based on inter-class separability DAG-SVM

作　　者：黄振龙[1] 郑骏[1] 胡文心[1] 

机构地区：[1]华东师范大学计算中心,上海200062

出　　处：《华东师范大学学报（自然科学版）》2013年第3期209-218,共10页Journal of East China Normal University(Natural Science)

基　　金：国家863项目(2011AA01A107);上海市科委重大科技攻关项目(10dz1500103;11511502203)

摘　　要：本方法采用了以类间分布和类间中心距离作为依据,对有向无环图结构进行调整,以解决传统的DAG-SVM多分类结构固定、单个节点位置随意引起的"误差累积"严重的缺陷.实验表明,该改进后的DAG-SVM文本分类方法,对文本分类准确率有一定的提高.This paper took an improved algorithm based on inter-class separability directed acyclic graph support vector machine（DAG-SVM） for text classification.The method has adjusted the DAG structure according to inter-class distribution and the distance between centers.It has solved the problems of fixed structure and random single node location in traditional DAG-SVM multi-classification method.The experiments show that the algorithm has improved the accuracy.

关 键 词：文本分类 支持向量机 DAG-SVM 类间可分性 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]