| Presentation | 2002/10/10 Research Development of Ensemble Learning Naonori UEDA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | It has been empirically or theoretically shown that a better learning machine with high generalization performance can be obtained by combining outputs of multiple learning machines. This is called, ensemble learning, a practical framework for constructing predictors with high generalization ability. In this tutorial, first, I explain the basic idea of ensemble learning and introduce several representative ensemble learning methods. I also give some intuitive and theoretical reasons why ensemble learning can improve generalization performance in some cases. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Ensemble Learning / Pattern Classification / Bayesian Learning |
| Paper # | NC2002-49 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2002/10/10(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Vice Chair | |
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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) | Research Development of Ensemble Learning |
| Sub Title (in English) | |
| Keyword(1) | Ensemble Learning |
| Keyword(2) | Pattern Classification |
| Keyword(3) | Bayesian Learning |
| 1st Author's Name | Naonori UEDA |
| 1st Author's Affiliation | NTT Communication Science Laboratories() |
| Date | 2002/10/10 |
| Paper # | NC2002-49 |
| Volume (vol) | vol.102 |
| Number (no) | 381 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |