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Document Classification Method with Small Training Data
Yasunari MAEDA,Hideki YOSHIDA,Toshiyasu MATSUSHIMA 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Document classification is one of important topics in the field of NLP(Natural Language Processing). In our previous research we’ve proposed a document classification method which minimizes an error rate with reference to a Bayes criterion. But when the number of documents in training data is small, the accuracy of the previous method is low. So in this research we propose a document classification method whose accuracy is higher than the previous method when the number of documents in training data is small.