Presentation | 2001/2/2 A Study on Theoretical Modeling of Ensemble Learning Masato UCHIDA, Hiroyuki SHIOYA, Tsutomu DA-TE, |
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Abstract(in English) | The ensemble learning using the expectation of total outputs from some trained machines is proposed to improve the predictive performance. In this paper, we analyze the algorithmic structure of this learning method using the negative logarithm likelihood function. As a result, we can realize the ensemble learning is a simple three-step-minimizing procedure for Kullback divergence. Taking this into account, the asymptotic statistical property is given to elucidate the effect of the ensemble learning. Our framework extends the theoretical range of the ensemble learning, that is, the ensemble learning using α-divergence can be defined in a similar way. In addition, we mention that the learning with non-Bayesian additive term is related to this framework. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | ensemble learning / expectation of predictive error / α-divergence |
Paper # | NC2000-95 |
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Committee | NC |
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Conference Date | 2001/2/2(1days) |
Place (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) | A Study on Theoretical Modeling of Ensemble Learning |
Sub Title (in English) | |
Keyword(1) | ensemble learning |
Keyword(2) | expectation of predictive error |
Keyword(3) | α-divergence |
1st Author's Name | Masato UCHIDA |
1st Author's Affiliation | Graduate School of Engineering, Hokkaido University() |
2nd Author's Name | Hiroyuki SHIOYA |
2nd Author's Affiliation | Graduate School of Engineering, Hokkaido University |
3rd Author's Name | Tsutomu DA-TE |
3rd Author's Affiliation | Graduate School of Engineering, Hokkaido University |
Date | 2001/2/2 |
Paper # | NC2000-95 |
Volume (vol) | vol.100 |
Number (no) | 618 |
Page | pp.pp.- |
#Pages | 7 |
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