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
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;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5485482
