Presentation 1998/6/18
Active Learning for Noise surpression
Masashi SUGIYAMA, Hidemitsu OGAWA,
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Abstract(in English) If we choose training data which provide the optimal generalization ability, we can carry out the learning effectively. In the presence of noise, it is also important to surpress noise influence. In this paper, we give a training data selection method for minimizing the noise variance.
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Keyword(in English) active learning / incremental projection learning / noise surpression / generalization ability / training data selection / projection learning
Paper # NC98-21,HIP98-12
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Committee HIP
Conference Date 1998/6/18(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Active Learning for Noise surpression
Sub Title (in English)
Keyword(1) active learning
Keyword(2) incremental projection learning
Keyword(3) noise surpression
Keyword(4) generalization ability
Keyword(5) training data selection
Keyword(6) projection learning
1st Author's Name Masashi SUGIYAMA
1st Author's Affiliation Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology()
2nd Author's Name Hidemitsu OGAWA
2nd Author's Affiliation Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology
Date 1998/6/18
Paper # NC98-21,HIP98-12
Volume (vol) vol.98
Number (no) 130
Page pp.pp.-
#Pages 8
Date of Issue