Presentation 2002/10/10
Research Development of Ensemble Learning
Naonori UEDA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) It has been empirically or theoretically shown that a better learning machine with high generalization performance can be obtained by combining outputs of multiple learning machines. This is called, ensemble learning, a practical framework for constructing predictors with high generalization ability. In this tutorial, first, I explain the basic idea of ensemble learning and introduce several representative ensemble learning methods. I also give some intuitive and theoretical reasons why ensemble learning can improve generalization performance in some cases.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Ensemble Learning / Pattern Classification / Bayesian Learning
Paper # NC2002-49
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Conference Information
Committee NC
Conference Date 2002/10/10(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) Research Development of Ensemble Learning
Sub Title (in English)
Keyword(1) Ensemble Learning
Keyword(2) Pattern Classification
Keyword(3) Bayesian Learning
1st Author's Name Naonori UEDA
1st Author's Affiliation NTT Communication Science Laboratories()
Date 2002/10/10
Paper # NC2002-49
Volume (vol) vol.102
Number (no) 381
Page pp.pp.-
#Pages 6
Date of Issue