Presentation | 2004/3/10 Evaluation of Self-Organizing Maps based on Multinomial Distribution Noriaki FUKUDA, Kazumi SAITO, Masumi ISHIKAWA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we study how similarity definitions and neighborhood definitions in self-organizing maps (SOM) affect their classification performance. Conventional SOM uses a combination of a standard Euclidean norm for document similarity and a neighborhood definition based on a Gaussian distribution. We propose a novel SOM which adopts logarithmic likelihood based on multinomial distributions for document similarity, and neighborhood definition based on multinomial distributions. In computer experiments using three kinds of benchmark document data sets, we evaluate these SOM models from the points of view of the classification performances and resulting self-organizing maps. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | self-organizing map / text mining / multinomial distribution |
Paper # | NC2003-143 |
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Committee | NC |
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Conference Date | 2004/3/10(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Evaluation of Self-Organizing Maps based on Multinomial Distribution |
Sub Title (in English) | |
Keyword(1) | self-organizing map |
Keyword(2) | text mining |
Keyword(3) | multinomial distribution |
1st Author's Name | Noriaki FUKUDA |
1st Author's Affiliation | Nagaoka University of Technology() |
2nd Author's Name | Kazumi SAITO |
2nd Author's Affiliation | NTT Communication Science Laboratories |
3rd Author's Name | Masumi ISHIKAWA |
3rd Author's Affiliation | Kyushu Institute of Technology |
Date | 2004/3/10 |
Paper # | NC2003-143 |
Volume (vol) | vol.103 |
Number (no) | 732 |
Page | pp.pp.- |
#Pages | 6 |
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