Presentation | 2005/3/23 Analysis of Ensemble Learning for Non-Monotonic Teacher Seiji MIYOSHI, Kazuyuki HARA, Masato OKADA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A major merit of ensemble learning is to realize the input-output relations by combining students that cannot be represented by one student. Therefore, ensemble learning in which a teacher isn't in the model space of one student is very attractive. In this paper ensemble learning, in which a teacher and students are a non-monotonic perceptron and simple perceptrons respectively, is discussed based on online learning theory and statistical mechanics. Hebbian learning doesn't keep the variety of students and the effect of ensemble disappears. On the contrary, perceptron learning keeps the variety of students and the effect of ensemble doesn't disappear. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | ensemble learning / online learning / non-monotonic teacher / generalization error |
Paper # | NC2004-214 |
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Committee | NC |
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Conference Date | 2005/3/23(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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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) | Analysis of Ensemble Learning for Non-Monotonic Teacher |
Sub Title (in English) | |
Keyword(1) | ensemble learning |
Keyword(2) | online learning |
Keyword(3) | non-monotonic teacher |
Keyword(4) | generalization error |
1st Author's Name | Seiji MIYOSHI |
1st Author's Affiliation | Kobe City College of Technology() |
2nd Author's Name | Kazuyuki HARA |
2nd Author's Affiliation | Tokyo Metropolitan College of Technology |
3rd Author's Name | Masato OKADA |
3rd Author's Affiliation | Division of Transdisciplinary Sciences, Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute:JST PRESTO |
Date | 2005/3/23 |
Paper # | NC2004-214 |
Volume (vol) | vol.104 |
Number (no) | 760 |
Page | pp.pp.- |
#Pages | 6 |
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