An Optimal Text Categorization Algorithm Based on SVM

摘要：

Text Categorization is the process of assigning documents to a set of previously fixed categories. In this paper we develop an optimal SVM algorithm for text classification via multiple optimal strategies, such as a novel importance weight definition, the feature selection using the likelihood ratio for binomial distribution, the optimal parameter settings, etc. Comparison between our method and other conventional text classification algorithms is conducted on Reuter and TREC corpora. The experimental results indicate that our proposed algorithm yields much better performance than other conventional algorithms.

作者: Ziqiang Wang Xia Sun Dexian Zhang

作者单位: School of Information and Engineering Henan University of Technology Zheng Zhou 450052, Henan Province, China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 2137-2140

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）