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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maeda, Y. Yoshida, H. Matsushima, T. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Computer Sciences, Kitami Institute of Technology, Hokkaido, Japan (Maeda, Y.; Yoshida, H.) || Department of Applied Mathematics, Waseda University, Tokyo, Japan (Matsushima, T.) |
| 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. |
| Starting Page | 138 |
| Ending Page | 141 |
| File Size | 130398 |
| Page Count | 4 |
| File Format | |
| ISBN | 9784907764340 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-18 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | SICE |
| Subject Keyword | Training data Error analysis Probability distribution Electronic mail Mathematics estimating data document classification small training data prior distributions |
| Content Type | Text |
| Resource Type | Article |
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