Presentation	2007-01-17 Self-Organizing Map Considering False Neighboring Neuron and its Applications Haruna MATSUSHITA, Yoshifumi NISHIO,
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Abstract(in English)	In the real world, it is not always true that the next-door house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new Self-Organizing Map (SOM) algorithm which considers the False Neighboring Neuron (called FNN-SOM). The FNN-SOM self-organizes with considering the real neighboring relation. The behavior of FNN-SOM is investigated with learning for various input data. We confirm that we can obtain the more effective map reflecting the distribution state of input data than the conventional SOM.
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Keyword(in English)	self-organizing maps (SOM) / feature extraction / clustering
Paper #	NLP2006-110
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Conference Information
Committee	NLP
Conference Date	2007/1/10(1days)
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Paper Information
Registration To	Nonlinear Problems (NLP)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Self-Organizing Map Considering False Neighboring Neuron and its Applications
Sub Title (in English)
Keyword(1)	self-organizing maps (SOM)
Keyword(2)	feature extraction
Keyword(3)	clustering
1st Author's Name	Haruna MATSUSHITA
1st Author's Affiliation	Department of Electrical and Electronic Engineering, Tokushima University()
2nd Author's Name	Yoshifumi NISHIO
2nd Author's Affiliation	Department of Electrical and Electronic Engineering, Tokushima University
Date	2007-01-17
Paper #	NLP2006-110
Volume (vol)	vol.106
Number (no)	451
Page	pp.pp.-
#Pages	4
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