Presentation	2010-12-13 Influence of correlated patterns in dynamical associative network using SVM Akinori KATO, Masaharu ADACHI,
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Abstract(in English)	In this paper, we investigate influence of correlated patterns in dynamical associative network using nonlinear SVM. Recall performance of dynamical associative memory is improved by using nonlinear Support Vector Machines (SVMs) instead of linear SVMs [1]. We introduce chaotic model neurons as the constituents of the associative memory model with nonlinear SVMs. Numerical experiments show that the high recall frequency with orbital instability can be realized even if there are correlations among stored patterns.
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Keyword(in English)	Support Vector Machine / Dynamical Associative Memory / Chaotic Neural Network
Paper #	NLP2010-117
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Committee	NLP
Conference Date	2010/12/6(1days)
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Registration To	Nonlinear Problems (NLP)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Influence of correlated patterns in dynamical associative network using SVM
Sub Title (in English)
Keyword(1)	Support Vector Machine
Keyword(2)	Dynamical Associative Memory
Keyword(3)	Chaotic Neural Network
1st Author's Name	Akinori KATO
1st Author's Affiliation	Department of Electrical and Electronic Engineering, Graduate School of Engineering, Tokyo Denki University()
2nd Author's Name	Masaharu ADACHI
2nd Author's Affiliation	Department of Electrical and Electronic Engineering, Graduate School of Engineering, Tokyo Denki University
Date	2010-12-13
Paper #	NLP2010-117
Volume (vol)	vol.110
Number (no)	335
Page	pp.pp.-
#Pages	6
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