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题名:
text classification using semi-supervised clustering
作者: Zhang Wen ; Yoshida Taketoshi ; Tang Xijin
会议文集: 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
会议名称: 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
会议日期: 37461
出版日期: 2009
会议地点: Beijing, China
关键词: Classification (of information) ; Maximum principle ; Optimization ; Support vector machines
出版地: United States
收录类别: EI
ISBN: 9780769537054
部门归属: (1) School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Ashahidai, Tatsunokuchi, Ishikawa 923-1292, Japan; (2) Lab. for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (3) Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China
英文摘要: In this paper, mixture models are used to classify documents. The basic assumption for the documents in a collection is that each class is composed of a number of mixture components. By indentifying the components in the document collection, the classes of documents can thereby be identified from each other. A semi-supervised clustering method is proposed to identify the components (clusters), and further, unlabeled data is used to produce more accurate clusters in document collection to correspond the components of document classes. Experimental results show that the proposed method produces better performances than support vector machine (SVM) with linear kernel, and produces comparable performance with Bayesian classifier with Expectation Maximization (EM) in text classification. © 2009 IEEE.
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/8520
Appears in Collections:互联网软件技术实验室 _会议论文

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Recommended Citation:
Zhang Wen,Yoshida Taketoshi,Tang Xijin. text classification using semi-supervised clustering[C]. 见:2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009. Beijing, China. 37461.
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