Presentation 1997/11/17
Error Correcting Memorization Learning for Noisy Training Data
Akiko NAKASHIMA, Akira HIRABAYASHI, Hidemitsu OGAWA,
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Abstract(in English) Multi-layer feedforward neural networks are trained using the error back-propagation algorithm. The algorithm minimizes the error between outputs of a neural network and training data. Hence, in the case of noisy training data, a trained network memorizes noisy outputs for given inputs. Such learning is called rote memorization learning. In this paper we propose error correcting memorization learning (CML) to suppress noise in training data. We clarify the mechanism of noise suppression of CML.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) memorization learning / back-propagation method / noisy training data / suppression of noise
Paper # NC97-51
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Conference Information
Committee NC
Conference Date 1997/11/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Error Correcting Memorization Learning for Noisy Training Data
Sub Title (in English)
Keyword(1) memorization learning
Keyword(2) back-propagation method
Keyword(3) noisy training data
Keyword(4) suppression of noise
1st Author's Name Akiko NAKASHIMA
1st Author's Affiliation Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology()
2nd Author's Name Akira HIRABAYASHI
2nd Author's Affiliation Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology
3rd Author's Name Hidemitsu OGAWA
3rd Author's Affiliation Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology
Date 1997/11/17
Paper # NC97-51
Volume (vol) vol.97
Number (no) 379
Page pp.pp.-
#Pages 8
Date of Issue