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Please use this identifier to cite or link to this item: http://hdl.handle.net/10445/5279

Title: Learning from Imperfect Data
Authors: Hartono, Pitoyo
Hashimoto, Shuji
Abstract: 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.
Research Achievement Classification: 原著論文/Original Paper
Type: Journal Article
Peer Review: あり/yes
Solo/Joint Author(s): 共著/joint
Published journal or presented
academic conference: 
Applied Soft Computing Journal
Volume: 7
Number: 1
Spage: 353
Epage: 363
Date: 2007
Publisher: Elsevier
Appears in Collections:Pitoyo, Hartono

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