Web Page Classification Based on SVM

Author

Weimin Xue ; Hong Bao ; Weitong Huang ; Yuchang Lu

Author_Institution

Inst. of Inf. Technol., Beijing Union Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6111

Lastpage

6114

Abstract

This paper studies several key aspects of support vector machine (SVM) for Web page classification. Developed from statistical learning theory, SVM is widely investigated and used for text categorization because of its high generalization performance and tolerant ability of processing high dimension classification. Firstly, some methods for Web page presentation are studied. Secondly the Web page classification based on SVM is implementation on data set, and NB classifier is used for study the performance of the SVM classifier processing high dimension space. Finally, the comparison on the polynomial kernel function and the radius basis function (RBF) kernel function is studied. It is proved that if a kernel has a perfect alignment with the classification task, the SVM classifier has better performances

Keywords

Bayes methods; Internet; classification; radial basis function networks; statistical analysis; support vector machines; SVM classifier; Web page classification; Web page presentation; naive Bayes classifier; polynomial kernel function; radius basis function kernel function; statistical learning theory; support vector machine; text categorization; HTML; Kernel; Niobium; Polynomials; Risk management; Sun; Support vector machine classification; Support vector machines; Text categorization; Web pages; Kernel function; Web page classification; radius basis function; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714255

Filename

1714255