text classification using semi-supervised clustering

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	text classification using semi-supervised clustering
	Zhang Wen; Yoshida Taketoshi; Tang Xijin
	2009
会议名称	2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
会议录名称	2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
会议日期	37461
会议地点	Beijing, China
收录类别	EI
出版地	United States
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
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.
关键词	Classification (Of Information) Maximum Principle Optimization Support Vector Machines
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8520
专题	互联网软件技术实验室
推荐引用方式 GB/T 7714	Zhang Wen,Yoshida Taketoshi,Tang Xijin. text classification using semi-supervised clustering[C]. United States,2009.

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