Title :
Study on SVM Compared with the other Text Classification Methods
Author :
Liu, Zhijie ; Lv, Xueqiang ; Liu, Kun ; Shi, Shuicai
Author_Institution :
Chinese Inf. Process. Res. Center, Beijing Inf. Sci. & Technol. Univ., Beijing, China
Abstract :
Based on the text information processing, we have made a study on the application of support vector machine in text categorization. Through introducing the basic principle of SVM, we described the process of text classification and further proposed a SVM-based classification model. Finally, experimental data show that F1 value of SVM classifier has reached more than 86.26%, and the classification results comparing to other classification methods have greatly improved, and it also proves that SVM is an effective machine learning method.
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; text analysis; SVM; SVM classifier; machine learning method; text categorization; text classification methods; text information processing; Bayesian methods; Decision trees; Educational technology; Information processing; Information science; Information technology; Learning systems; Support vector machine classification; Support vector machines; Text categorization; SVM; machine learning; text classification;
Conference_Titel :
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6388-6
Electronic_ISBN :
978-1-4244-6389-3
DOI :
10.1109/ETCS.2010.248
