计算机与现代化 ›› 2012, Vol. 198 ›› Issue (2): 1-4.doi: 10.3969/j.issn.1006-2475.2012.02.001
• 算法设计与分析 • 下一篇
陈琳，王箭
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CHEN Lin，WANG Jian
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摘要： 网络信息规模随着互联网与信息技术的发展而不断增大，在这些信息中，各种类型的文本信息占据了相当大的比重。因此，高效、快速地对文本信息进行分类是网络信息处理中一个关键问题。本文分析比较了SVM算法、朴素Bayes算法和KNN算法3种算法，并通过实验证明了这3种算法在中文文本分类中的效果。实验结果表明：SVM算法比KNN算法和朴素Bayes算法更优，SVM算法是一种较好的中文文本分类算法。
关键词: 中文文本分类, SVM, Bayes, KNN
Abstract: With the development of Internet and information technology, network information scale is explosively increasing. Among various type of information, the type of texts occupy a considerable proportion. Therefore, efficient and rapid classification and processing of text information in the network become a key issue. The paper analyzes and compares SVM algorithm, Bayes algorithm and KNN algorithm. By the experiments of the three algorithms in Chinese text classification, the results indicate SVM algorithm is superior than KNN algorithm and Bayes algorithm, SVM algorithm is an excellent Chinese text classification algorithm.
Key words: Chinese text classification, SVM, Bayes, KNN
中图分类号:
TP301.6
陈琳;王箭. 三种中文文本自动分类算法的比较和研究[J]. 计算机与现代化, 2012, 198(2): 1-4.
CHEN Lin;WANG Jian. Comparison and Research on Algorithms of Three Chinese Text Classification[J]. Computer and Modernization, 2012, 198(2): 1-4.
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http://www.c-a-m.org.cn/CN/Y2012/V198/I2/1
