DocumentCode
2262688
Title
A new text classification method based on HMM-SVM
Author
Wang, Jing ; Yao, Yong ; Liu, Zhijing
Author_Institution
Xidian Univ., Xi´´an
fYear
2007
fDate
17-19 Oct. 2007
Firstpage
1516
Lastpage
1519
Abstract
Text classification has been considered as a hot research area in data mining. This paper presents a new approach combining hidden Markov model (HMM) with support vector machine (SVM) for text classification. HMMs are used to as a feature extractor and then a new feature vector is normalized as the input of SVMs, so the trained SVMs can classify unknown texts successfully. The experimental results demonstrate that the new method has a very high precision.
Keywords
data mining; feature extraction; hidden Markov models; support vector machines; text analysis; data mining; feature extraction; hidden Markov model; support vector machine; text classification method; Computer science; Data mining; Electronic mail; Feature extraction; Hidden Markov models; Nearest neighbor searches; Neural networks; Support vector machine classification; Support vector machines; Text categorization; Text classification; feature extraction; hidden Markov model; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications and Information Technologies, 2007. ISCIT '07. International Symposium on
Conference_Location
Sydney,. NSW
Print_ISBN
978-1-4244-0976-1
Electronic_ISBN
978-1-4244-0977-8
Type
conf
DOI
10.1109/ISCIT.2007.4392256
Filename
4392256
Link To Document