書誌事項
- タイトル別名
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- A Note on Document Classification with Small Training Data
- ガクシュウ データ ガ ショウリョウ シカ ナイ バアイ ノ ブンショ ブンルイ ニ カンスル イチ コウサツ
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抄録
Document classification is one of important topics in the field of NLP (Natural Language Processing). In the previous research a document classification method has been proposed 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 use estimating data in order to estimate prior distributions. When the training data is small the accuracy using estimating data is higher than the accuracy of the previous method. But when the training data is big the accuracy using estimating data is lower than the accuracy of the previous method. So in this research we also propose another technique whose accuracy is higher than the accuracy of the previous method when the training data is small, and is almost the same as the accuracy of the previous method when the training data is big.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 131 (8), 1459-1466, 2011
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679584458368
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- NII論文ID
- 10030527175
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 11196040
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- IRDB
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可