文献类型：会议论文

英文题名：Research of Web Classification Mining Based on Classify Support Vector Machine

作者：Gao, Meijuan[1];Tian, Jingwen[1];Zhou, Shiru[1]

第一作者：高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

会议日期：AUG 08-09, 2009

会议地点：Sanya, PEOPLES R CHINA

语种：英文

外文关键词：Web mining; classification; genetic algorithm; support vector machine

摘要：With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the web document classification and the theory of artificial neural network, a web classification mining method based ann classify support vector machine (SVM) is presented in this paper. The SVM network structure that used for web text information classification is established, and we use the genetic algorithm (GA) to optimize SVM parameters, thereby enhancing the convergence rate and the classification accuracy. The structure of web classification mining system based on classify support vector machine is given. With the ability of strong pattern classification and self-learning and well generalization of SVM, the classification mining method can truly classify the web text information. The actual classification results show that this method is feasible and effective.