| Presentation | 2005-12-09 Learning algorithm by feature space reconstruction for the automatic recognition system Koichi Ikuta, Kenichi Tanaka, Kazuo Kyuma, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The visual inspection of the industrial product copes with defects that have wide variety of features in the shape, size, and strength. Most of the learning algorithms of the recognition system require specific training patterns for learning of the feature extraction filters. However, there are many cases that the recognition tasks don't have specific training patterns. In this paper, we propose a learning algorithm which reconstructs feature extraction filters on the basis of reinforcement signals. The recognition system constructed by the learning algorithm is robust against environmental variation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Reinforcement signal / Feature extraction / learning algorithm |
| Paper # | NC2005-88 |
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| Committee | NC |
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| Conference Date | 2005/12/2(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning algorithm by feature space reconstruction for the automatic recognition system |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement signal |
| Keyword(2) | Feature extraction |
| Keyword(3) | learning algorithm |
| 1st Author's Name | Koichi Ikuta |
| 1st Author's Affiliation | Advanced Technology R&D Center Mitsubishi Electric Corporation() |
| 2nd Author's Name | Kenichi Tanaka |
| 2nd Author's Affiliation | Advanced Technology R&D Center Mitsubishi Electric Corporation |
| 3rd Author's Name | Kazuo Kyuma |
| 3rd Author's Affiliation | Advanced Technology R&D Center Mitsubishi Electric Corporation |
| Date | 2005-12-09 |
| Paper # | NC2005-88 |
| Volume (vol) | vol.105 |
| Number (no) | 457 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |