Document classification method with small training data

Yasunari Maeda*, Hideki Yoshida, Toshiyasu Matsushima

*この研究の対応する著者

研究成果

抄録

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.

本文言語English
ホスト出版物のタイトルICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
ページ138-141
ページ数4
出版ステータスPublished - 2009 12 1
イベントICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
継続期間: 2009 8 182009 8 21

出版物シリーズ

名前ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
国/地域Japan
CityFukuoka
Period09/8/1809/8/21

ASJC Scopus subject areas

  • 情報システム
  • 制御およびシステム工学
  • 産業および生産工学

引用スタイル