Presentation 1999/11/26
Incremental Learning for Optimal Generalization in a Family of Projection Learings
Eiji NISHI, Masashi SUGIYAMA, Hidemitsu OGAWA,
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Abstract(in English) We consider the case where new training examples are added to acquire a higher level of generalization capability after the learning process has been completed. In this case, incremental learning is generally preferred because it is more efficient in computation than batch learning. In this paper, we propose an incremental learning method for a family of projection learnings, which is a collection of an infinite kinds of learnings including projection learning, partial projection learning, and averaged projection learning, The proposed method is optimal in the sense of the generalization capability, i. e., it provides exactly the same generalization capability as that obtained by batch learning.
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Keyword(in English) supervised learning / generalization capability / a family of projection learnings / batch learning / incremental learning
Paper # NC99-55
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Committee NC
Conference Date 1999/11/26(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Incremental Learning for Optimal Generalization in a Family of Projection Learings
Sub Title (in English)
Keyword(1) supervised learning
Keyword(2) generalization capability
Keyword(3) a family of projection learnings
Keyword(4) batch learning
Keyword(5) incremental learning
1st Author's Name Eiji NISHI
1st Author's Affiliation Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology.()
2nd Author's Name Masashi SUGIYAMA
2nd Author's Affiliation Department of Computer Science, Tokyo Institute of Technology.
3rd Author's Name Hidemitsu OGAWA
3rd Author's Affiliation Department of Computer Science, Tokyo Institute of Technology.
Date 1999/11/26
Paper # NC99-55
Volume (vol) vol.99
Number (no) 473
Page pp.pp.-
#Pages 8
Date of Issue