| Presentation | 1998/6/18 Active Learning for Nosie Surpression Masashi SUGIYAMA, Hidemitsu OGAWA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | If we choose training data which provide the optimal generalization ability, we can carry out the learning effectively. In the presence of noise, it is also important to supress noise influence. In this paper, we give a training data selection method for minimizing the noise variance. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | active learning / incremental projection learning / noise supression / generalization ability / training data selection / projection learning |
| Paper # | NC98-21,HIP98-12 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 1998/6/18(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| 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) | Active Learning for Nosie Surpression |
| Sub Title (in English) | |
| Keyword(1) | active learning |
| Keyword(2) | incremental projection learning |
| Keyword(3) | noise supression |
| Keyword(4) | generalization ability |
| Keyword(5) | training data selection |
| Keyword(6) | projection learning |
| 1st Author's Name | Masashi SUGIYAMA |
| 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 | 1998/6/18 |
| Paper # | NC98-21,HIP98-12 |
| Volume (vol) | vol.98 |
| Number (no) | 128 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |