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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10445/5279

タイトル: Learning from Imperfect Data
著者: Hartono, Pitoyo
Hashimoto, Shuji
アブストラクト: For a supervised learning method, the quality of the training data or the training supervisor is very important in generating reliable neural networks. However, for real world problems, it is not always easy to obtain high quality training data sets. In this research, we propose a learning method for a neural network ensemble model that can be trained with an imperfect training data set, which is a data set containing erroneous training samples. With a competitive training mechanism, the ensemble is able to exclude erroneous samples from the training process, thus generating a reliable neural network. Through the experiment, we show that the proposed model is able to tolerate the existence of erroneous training samples in generating a reliable neural network. The ability of the neural network to tolerate the existence of erroneous samples in the training data lessens the costly task of analyzing and arranging the training data, thus increasing the usability of the neural networks for real world problems.
研究業績種別: 原著論文/Original Paper
資料種別: Journal Article
査読有無: あり/yes
単著共著: 共著/joint
発表雑誌名,発表学会名など: Applied Soft Computing Journal
巻: 7
号: 1
開始ページ: 353
終了ページ: 363
年月日: 2007年
出版社: Elsevier
出現コレクション:ピトヨ・ハルトノ

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