Document classification method with small training data
Yasunari Maeda, Hideki Yoshida, Toshiyasu Matsushima · 2009 ICCAS-SICE · 2009
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.