Ensemble Learning of Linear Perceptrons : On-Line Learning Theory(General)
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概要
- 論文の詳細を見る
We analyze ensemble learning including the noisy case where teacher or student noise is present. Linear perceptrons are used as teacher and student. First, we analyze the homogeneous correlation of initial weight vectors. The generalization error consists of two parts : the first term depends on the number of perceptrons K and is proportional to 1/K, the second does not depend on K in the first case. In the inhomogeneous correlation of initial weight vectors case, the weighted average could be optimized to minimize the generalization error. We found that the optimal weights do not depend on time without student noise, while the optimal weights depend on time and become 1/K with student noise.
- 社団法人日本物理学会の論文
- 2005-11-15
著者
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Hara Kazuyuki
Tokyo Metropolitan Coll. Of Industrial Technol. Tokyo
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Hara Kazuyuki
Tokyo Metropolitan College Of Technology
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Okada Masato
Graduate School Of Frontier Sciences The University Of Tokyo
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Okada Masato
Graduate School Of Engineering Science Osaka University
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OKADA Masato
Graduate School of Frontier Sciences, The University of Tokyo:Laboratory for Mathematical Neuroscience, Brain Science Institute, RIKEN:Intelligent Corporation and Control, PRESTO, Japan Science and Technology Agency
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