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Improving Linear Classifier for Chinese Text Categorization

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題名: Improving Linear Classifier for Chinese Text Categorization

作者: Jyh-Jong Tsay;Jing-Doo Wang

關鍵詞: Information retrieval;Linear classifier;Text categorization

日期: 2004

上傳時間: 2010-04-15T05:42:24Z

摘要: 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.

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