Presentation 2008-02-01
Batch-Learning Self-Organizing Map with False-Neighbor Degree for Effective Self-Organization
Haruna MATSUSHITA, Yoshifumi NISHIO,
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Abstract(in English) This study proposes a Batch-Learning Self-Organizing Map with False-Neighbor degree between neurons (called BL-FNSOM). False-neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false-neighbor degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BL-FNSOM changes the neighborhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) self-organizing maps (SOM) / clustering / unsupervised learning
Paper # NLP2007-150
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Conference Information
Committee NLP
Conference Date 2008/1/25(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Batch-Learning Self-Organizing Map with False-Neighbor Degree for Effective Self-Organization
Sub Title (in English)
Keyword(1) self-organizing maps (SOM)
Keyword(2) clustering
Keyword(3) unsupervised learning
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 2008-02-01
Paper # NLP2007-150
Volume (vol) vol.107
Number (no) 478
Page pp.pp.-
#Pages 6
Date of Issue