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    Title: Improving Linear Classifier for Chinese Text Categorization
    Authors: 王經篤;Wang, Jing-Doo
    Contributors: 資訊工程學系
    Keywords: Information retrieval;Linear classifier;Text categorization
    Date: 2004-03
    Issue Date: 2012-11-26 13:54:51 (UTC+8)
    Abstract: The goal of this paper is to derive extra representatives from each class to compensate for the potential weakness of linear classifiers that compute one representative for each class. To evaluate the effectiveness of our approach, we compared with linear classifier produced by Rocchio algorithm and the k-nearest neighbor (kNN) classifier. Experimental results show that our approach improved linear classifier and achieved micro-averaged accuracy close to that of kNN, with much less classification time. Furthermore, we could provide a suggestion to reorganize the structure of classes when identify new representatives for linear classifier.
    Relation: Information Processing and Management
    Appears in Collections:[Department of Computer Science and Information Engineering] Journal Artical

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