Presentation | 2000/2/3 Adaptive Incremental Projection Learning Yoshiharu Suga, Hidemitsu Ogawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | When a target function has changed after the learning was finished, the learning result becomes wrong obviously. Then, we have to learn the new target function by using new training examples. It is called re-learning. However, if the change is partial, we can learn more effectively than re-learning. In this paper, we give an incremental learning method which utilizes not only new training examples but also the former learning result to provide better generalization ability than re-learning. The proposed method is successfully applied to the problem of learning the sensorimotor maps of a robot arm. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | projection learning / re-learning / incremental learning / generalization ability / sensorimotor maps |
Paper # | NC99-82 |
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Committee | NC |
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Conference Date | 2000/2/3(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) | Adaptive Incremental Projection Learning |
Sub Title (in English) | |
Keyword(1) | projection learning |
Keyword(2) | re-learning |
Keyword(3) | incremental learning |
Keyword(4) | generalization ability |
Keyword(5) | sensorimotor maps |
1st Author's Name | Yoshiharu Suga |
1st Author's Affiliation | Department of Computer Science, Graduate School of Infomation Science and Engineering, Tokyo Institute of Technology.() |
2nd Author's Name | Hidemitsu Ogawa |
2nd Author's Affiliation | Department of Computer Science, Graduate School of Infomation Science and Engineering, Tokyo Institute of Technology. |
Date | 2000/2/3 |
Paper # | NC99-82 |
Volume (vol) | vol.99 |
Number (no) | 612 |
Page | pp.pp.- |
#Pages | 8 |
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