DocumentCode
2650250
Title
A new text categorization method based on HMM and SVM
Author
Donghui, Chen ; Zhijing, Liu
Author_Institution
Sch. of Comput. Sci. & Technol., Xidian Univ., Xi´´an, China
Volume
7
fYear
2010
fDate
16-18 April 2010
Abstract
This paper has put forward a new method to improve the performance of text categorization. The new method combines HMM (Hidden Markov Model) and SVM (Support Vector Machines). 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 prove that the method is more effective and high classification accuracy.
Keywords
feature extraction; hidden Markov models; pattern classification; support vector machines; text analysis; feature extractor; feature vector; hidden Markov model; support vector machines; text categorization method; text classification; Computer science; Data mining; Feature extraction; Hidden Markov models; Nearest neighbor searches; Neural networks; Paper technology; Support vector machine classification; Support vector machines; Text categorization; feature selection; hidden markov model; support vector machines; text categorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485482
Filename
5485482
Link To Document