Presentation | 2002/3/12 Meta Learning for Quick Incremental Learning Takayuki OHIRA, Koichiro YAMAUCHI, Takashi OMORI, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The neural networks need a high technique to deal with incremental learning. This is because one parameter of the system plays to record not only one instance but also several different instances. To overcome the problem, several systems, which have a buffer beside of the neural network, have been proposed. The buffer stores some of old presented instances. The neural network learns a new instance with the stored instances in the buffer. However, with this technique, the learner wastes a long time to learn the instances in the buffer. To overcome the drawback, we propose here a novel technique to use a meta-learning network, which learns a strategy for the incremental learning. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | quick incremental learning / meta-learning / learning to learn / learning strategy / RBF / RAN |
Paper # | NC2001-178 |
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Committee | NC |
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Conference Date | 2002/3/12(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) | Meta Learning for Quick Incremental Learning |
Sub Title (in English) | |
Keyword(1) | quick incremental learning |
Keyword(2) | meta-learning |
Keyword(3) | learning to learn |
Keyword(4) | learning strategy |
Keyword(5) | RBF |
Keyword(6) | RAN |
1st Author's Name | Takayuki OHIRA |
1st Author's Affiliation | Faculty of Engineering, Hokkaido University() |
2nd Author's Name | Koichiro YAMAUCHI |
2nd Author's Affiliation | Faculty of Engineering, Hokkaido University |
3rd Author's Name | Takashi OMORI |
3rd Author's Affiliation | Faculty of Engineering, Hokkaido University |
Date | 2002/3/12 |
Paper # | NC2001-178 |
Volume (vol) | vol.101 |
Number (no) | 736 |
Page | pp.pp.- |
#Pages | 7 |
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