自动文本分类技术研究

随着万维网的发展,文本分类成为处理和组织大量文档数据的关键技术。在阐述了文本分类算法的研究现状,分析了朴素贝叶斯(Na ve Bayes)、kNN和支持向量机(SVM)经典文本分类算法之后,提出了应用最小二乘支持向量机(LSSVM)算法来实现文本分类。对使用用最小二乘支持向量机和一般支持向量机的文本分类结果进行了比较,并得出了结论:使用最小二乘支持向量机进行文本分类缩短了文本分类的时间,并保证了一定的召回率和准确率。

A Survey of Algorithm of Text Categorization

With the rapid development of World Wide Web,text classification has become the key technology in organizing and processing large amount of document data.After prolific research results in this field are illustrated involving in typical algorithms Which include Naive Bayes,kNN and Support Vector Machine(SVM),it is presented that realization of text categorization with LS-SVM.The experimental result shows that the performance of LS-SVM is comparable to common SVM with much reduction on the time cost in the text categorizaion process.