Presentation | 1997/11/17 Noise Suppression of Training Data and Generalization Ability Akiko NAKASHIMA, Hidemitsu OGAWA, |
---|---|
PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Multi-layer feedforward neural networks are trained using the error back-propagation(BP) algorithm. The algorithm minimizes the training error. Hence, in the case of noisy training data, a trained network memorizes noisy outputs for given inputs. In order to suppress noise in training data, we proposed error correcting memorization learning(CML). In this paper, we evaluate generalization ability of CML comparing with the projection learning (PL). It is theoretically proved that although CML merely suppresses noise in training data, it provides the same generalization as PL under some necessary and sufficient condition. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | generalization / memorization learning / suppression of noise / admissibility / back-propagation |
Paper # | NC97-52 |
Date of Issue |
Conference Information | |
Committee | NC |
---|---|
Conference Date | 1997/11/17(1days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | |
Vice Chair | |
Secretary | |
Assistant |
Paper Information | |
Registration To | Neurocomputing (NC) |
---|---|
Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Noise Suppression of Training Data and Generalization Ability |
Sub Title (in English) | |
Keyword(1) | generalization |
Keyword(2) | memorization learning |
Keyword(3) | suppression of noise |
Keyword(4) | admissibility |
Keyword(5) | back-propagation |
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 | Hidemitsu OGAWA |
2nd Author's Affiliation | Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology |
Date | 1997/11/17 |
Paper # | NC97-52 |
Volume (vol) | vol.97 |
Number (no) | 379 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |