DocumentCode
424091
Title
The hierarchical classification of Web content by the combination of textual and visual features
Author
Dong, Shou-Bin
Author_Institution
Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
Volume
3
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
1524
Abstract
This paper presents the hierarchical classification of Web content based on the combination of both textual and visual features. This combination is achieved by multiple classifier combination. A schema based on adaptive category weighting is proposed for achieving good combination, which has gained better results compared to the ordinary combination based on general voting schema.
Keywords
Internet; feature extraction; image classification; principal component analysis; support vector machines; Web content; adaptive category weighting; hierarchical classification; multiple classifier combination; principal component analysis; support vector machines; textual features; visual features; Computer science; Data mining; Electronic mail; Feature extraction; Internet; Machine learning; Support vector machine classification; Support vector machines; Voting; Web pages;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382015
Filename
1382015
Link To Document