Presentation | 2008-03-28 Lazy Self-Organizing Map for Effective Self-Organization Taku HARAGUCHI, Haruna MATSUSHITA, Yoshifumi NISHIO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | self-organizing maps (SOM) / clustering / data mining |
Paper # | NLP2007-168 |
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Committee | NLP |
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Conference Date | 2008/3/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Lazy Self-Organizing Map for Effective Self-Organization |
Sub Title (in English) | |
Keyword(1) | self-organizing maps (SOM) |
Keyword(2) | clustering |
Keyword(3) | data mining |
1st Author's Name | Taku HARAGUCHI |
1st Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University() |
2nd Author's Name | Haruna MATSUSHITA |
2nd Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University |
3rd Author's Name | Yoshifumi NISHIO |
3rd Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University |
Date | 2008-03-28 |
Paper # | NLP2007-168 |
Volume (vol) | vol.107 |
Number (no) | 561 |
Page | pp.pp.- |
#Pages | 6 |
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