%0 Journal Article
%T Using SVM-based LLSI for text classification<br>使用基于SVM的局部潜在语义索引进行文本分类
%A ZHANG Qiu-yu
%A LIU Yang
%A <br>张秋余
%A 刘洋
%J 计算机应用
%D 2007
%I 
%X Latent Semantic Indexing (LSI) uses Singular Value Decomposition (SVD) to obtain latent semantic structure of original term-document matrix, and problems of polysemy and synonymy can be dealt with to some extent. However, the present available methods of applying LSI to text classification are not satisfying, since they do not take full account of classification information. To solve the problem, an improved Local LSI (LLSI) method was proposed, using Support Vector Machine (SVM) to produce the local region. Experimental results suggest that the proposed method is effective.
%K text classification
%K Latent Semantic Indexing (LSI)
%K Support Vector Machine (SVM)
%K local region<br>文本分类
%K 潜在语义索引
%K 支持向量机
%K 局部区域
%K 使用
%K 局部
%K 潜在语义索引
%K 文本分类
%K 结果
%K 实验
%K 区域
%K 支持向量机
%K 改进
%K 分类信息
%K 效果
%K 方法
%K 问题
%K 多词
%K 一词多义
%K 程度
%K 语义结构
%K 矩阵
%K 文档
%K 原始词
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD274305126D90F2AFD15D34B5&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=83525804D8B40525&eid=7A60741D2B519BE0&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=13