DocumentCode
457097
Title
An Efficient SVM Classifier for Lopsided Corpora
Author
Zhang, Xianfei ; Li, Bicheng ; WangShi ; LuoCheng
Author_Institution
Zhengzhou Inf. Sci. & Technol. Inst.
Volume
1
fYear
0
fDate
0-0 0
Firstpage
1144
Lastpage
1147
Abstract
This paper explores application of SVM to lopsided-corpora in text categorization. By means of integrating kernel caching with shrinking policies effectively, an improved SVM training algorithm for weight-calculation formula is proposed under the decomposition framework. Extensive experiments on lopsided-corpora have been conducted. The conclusion can make it possible to apply the improved SVM training algorithm to lopsided corpora in text categorization
Keywords
learning (artificial intelligence); pattern classification; support vector machines; text analysis; SVM classifier; SVM training algorithm; decomposition framework; kernel caching; lopsided corpora; shrinking policy; support vector machine; text categorization; weight-calculation formula; Constraint optimization; Kernel; Large-scale systems; Machine learning; Machine learning algorithms; Statistical learning; Support vector machine classification; Support vector machines; Text categorization; Web pages;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location
Hong Kong
ISSN
1051-4651
Print_ISBN
0-7695-2521-0
Type
conf
DOI
10.1109/ICPR.2006.242
Filename
1699092
Link To Document