Presentation 2005/3/23
Analysis of Ensemble Learning for Non-Monotonic Teacher
Seiji MIYOSHI, Kazuyuki HARA, Masato OKADA,
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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.
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Keyword(in English) ensemble learning / online learning / non-monotonic teacher / generalization error
Paper # NC2004-214
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Committee NC
Conference Date 2005/3/23(1days)
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Paper Information
Registration To Neurocomputing (NC)
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
Date of Issue