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 |
Event | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka Duration: 2009 Aug 18 → 2009 Aug 21 |
Other
Other | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 |
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City | Fukuoka |
Period | 09/8/18 → 09/8/21 |
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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
Document classification method with small training data. / Maeda, Yasunari; Yoshida, Hideki; Matsushima, Toshiyasu.
ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 138-141 5333327.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Document classification method with small training data
AU - Maeda, Yasunari
AU - Yoshida, Hideki
AU - Matsushima, Toshiyasu
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Document classification
KW - Estimating data
KW - Prior distributions
KW - Small training data
UR - http://www.scopus.com/inward/record.url?scp=77951142189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951142189&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77951142189
SN - 9784907764333
SP - 138
EP - 141
BT - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
ER -