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 language | English |
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| Title of host publication | ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings |
| Pages | 138-141 |
| Number of pages | 4 |
| Publication status | Published - 2009 Dec 1 |
| Event | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan Duration: 2009 Aug 18 → 2009 Aug 21 |
Publication series
| Name | ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings |
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Other
| Other | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 |
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| Country | Japan |
| City | Fukuoka |
| Period | 09/8/18 → 09/8/21 |
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).