Analysis of Ensemble Learning Using Simple Perceptrons Based on Online Learning Theory
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概要
- 論文の詳細を見る
Ensemble learning of K simple perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. Hebbian, perceptron and AdaTron learning show different characteristics in their affinity for ensemble learning, that is "maintaining variety among students". Results show that AdaTron learning is superior to the other two rules.
- 一般社団法人日本物理学会の論文
- 2005-04-30
著者
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Hara Kazuyuki
Tokyo Metropolitan Coll. Of Industrial Technol. Tokyo
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Miyoshi Seiji
Kobe City College Of Technology
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Okada Masato
Graduate School Of Engineering Science Osaka University
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Miyoshi Seiji
Kobe City Coll. Of Technol. Kobe
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Okada Masato
"Intelligent Coorperation and Control", PRESTO, JST
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OKADA Masato
RIKEN BSI:Japan Scientific Technology Corp.:Graduate School of Frontier Science, The University of Tokyo
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OKADA Masato
Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute:Intelligent Cooperation and Control
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