Presentation 2002/7/19
An Emergent Learning Method Capable of Training a Class of Pattern Classifiers in Polynomial Time and Space
Bao-Liang LU, Michinori ICHIKAWA,
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Abstract(in English) Emergent learning is a new learning method proposed in our previous work. In emergent learning, the solutions to a complex K-class classification problem emerged by simply combining the solutions of related smaller and simpler two-class subproblems, rather than directly solving the original complex K-class classification problem. In this paper we analyze the time and space complexity of training pattern classifiers with the emergent learning method. We demonstrate that the emergent learning method is capable of completely training a class of pattern classifers in both polynomial time and polynomial space.
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Keyword(in English) Emergent learning / task decomposition / module combination / parallel learning / min-max modular neural network / pattern classification
Paper # NC2002-42
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Committee NC
Conference Date 2002/7/19(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) An Emergent Learning Method Capable of Training a Class of Pattern Classifiers in Polynomial Time and Space
Sub Title (in English)
Keyword(1) Emergent learning
Keyword(2) task decomposition
Keyword(3) module combination
Keyword(4) parallel learning
Keyword(5) min-max modular neural network
Keyword(6) pattern classification
1st Author's Name Bao-Liang LU
1st Author's Affiliation Lab. for Brain-Operative Device, RIKEN Brain Science Institute()
2nd Author's Name Michinori ICHIKAWA
2nd Author's Affiliation Lab. for Brain-Operative Device, RIKEN Brain Science Institute
Date 2002/7/19
Paper # NC2002-42
Volume (vol) vol.102
Number (no) 253
Page pp.pp.-
#Pages 6
Date of Issue