Document classification method with small training data

Yasunari Maeda, Hideki Yoshida, Toshiyasu Matsushima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages138-141
Number of pages4
Publication statusPublished - 2009 Dec 1
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 2009 Aug 182009 Aug 21

Publication series

NameICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CountryJapan
CityFukuoka
Period09/8/1809/8/21

Keywords

  • Document classification
  • Estimating data
  • Prior distributions
  • Small training data

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Maeda, Y., Yoshida, H., & Matsushima, T. (2009). Document classification method with small training data. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 138-141). [5333327] (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).