Presentation 2003/10/16
Emergence of category-baias based learning learning acceleration in coherent neuralnetworks
Toshihiko HAMANO, Akira HIROSE,
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Abstract(in English) By regarding the carrier frequency as an internal state in coherent neural networks, such a function is realizable by a self-organizing mechanism that identical input yields different outputs according to its internal state. In this paper, we analyze the learning acceleration by introducing contextual I/O patterns as a category bias condition. In the result, on the category bias condition including the contextual I/O patterns, there is a steeper learning acceleration than on the non-category bias condition. This result shows the potentiality that, based on this system, a self-organizing informaiton system that has human-like learning function will be realized.
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Keyword(in English) category bias / acceleration / hierarchy
Paper # PRMU2003-115,NC2003-46
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Committee NC
Conference Date 2003/10/16(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) Emergence of category-baias based learning learning acceleration in coherent neuralnetworks
Sub Title (in English)
Keyword(1) category bias
Keyword(2) acceleration
Keyword(3) hierarchy
1st Author's Name Toshihiko HAMANO
1st Author's Affiliation Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo()
2nd Author's Name Akira HIROSE
2nd Author's Affiliation Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo
Date 2003/10/16
Paper # PRMU2003-115,NC2003-46
Volume (vol) vol.103
Number (no) 391
Page pp.pp.-
#Pages 6
Date of Issue