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- Sano Natsuki
- Central Research Institute of Electric Power Industry
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- Suzuki Hideo
- University of Tsukuba
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- Koda Masato
- University of Tsukuba
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抄録
Classifier is used for pattern recognition in various fields including data mining. Boosting is an ensemble learning method to boost (enhance) an accuracy of single classifier. We propose a new, robust boosting method by using a zero-one step function as a loss function. In deriving the method, the MarginBoost technique is blended with the stochastic gradient approximation algorithm, called Stochastic Noise Reaction (SNR). Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost on test error rates in the case of noisy, mislabeled situation.
収録刊行物
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- 日本オペレーションズ・リサーチ学会論文誌
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日本オペレーションズ・リサーチ学会論文誌 51 (1), 95-110, 2008
公益社団法人 日本オペレーションズ・リサーチ学会
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詳細情報 詳細情報について
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- CRID
- 1390001204109164672
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- NII論文ID
- 110006632507
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- NII書誌ID
- AA00703935
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- ISSN
- 21888299
- 04534514
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- NDL書誌ID
- 9421038
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可