Presentation	1999/11/26 Incremental Active Learning in Consideration of Bias Masashi Sugiyama, Hidemitsu Ogawa,
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Abstract(in English)	The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the sampling location minimizing the variance of the learning results is selected. This implies that the bias of the learning results is assumed to be zero or small enough to be neglected. In this paper, we propose an active learning method with the bias reduction. The effectiveness of the proposed method is demonstrated through computer simulations.
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Keyword(in English)	generalization capability / active learning / experimental design / incremental projection learning / bias/variance.
Paper #	NC99-56
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Conference Information
Committee	NC
Conference Date	1999/11/26(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	ENG
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Incremental Active Learning in Consideration of Bias
Sub Title (in English)
Keyword(1)	generalization capability
Keyword(2)	active learning
Keyword(3)	experimental design
Keyword(4)	incremental projection learning
Keyword(5)	bias/variance.
1st Author's Name	Masashi Sugiyama
1st Author's Affiliation	Department of Computer Science, Tokyo Institute of Technology()
2nd Author's Name	Hidemitsu Ogawa
2nd Author's Affiliation	Department of Computer Science, Tokyo Institute of Technology
Date	1999/11/26
Paper #	NC99-56
Volume (vol)	vol.99
Number (no)	473
Page	pp.pp.-
#Pages	8
Date of Issue