Performance Analysis of Chinese Webpage Categorizing Algorithm Based on Support Vector Machines (SVM)

Categorizing Web automatically for users is a key technique of information society, and the key point of this technique is Web training and categorization. This paper researches one of the important algorithm in this field-support vector machines (SVM). By analyzing and simulating 4 kinds of kernel function and 3 ways of feature selection, polynomial kernel function and document frequency is chosen for the best way in SVM algorithm. Meanwhile, pre-process algorithm is given in this paper in order to improve the efficiency of categorization. By simulation, importing pre-process method to SVM enhances the capability of the Web categorization both in precision and time-consumption.