Presentation | 2004/3/10 Learning Model of SOM Using Supervised Information Noriaki FUKUDA, Kazumi SAITO, Shinichi MATSUO, Masumi ISHIKAWA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we propose a novel learning model for constructing a supervised SOM (self-organizing map) with k-nearest neighbors. In this learning model, k-nearest neighboring samples are selected from input samples for each reference vector, then multiple labels are assigned for each reference vector corresponding to its k-nearest neighboring samples, finally SOM is constructed by using a sample division under the condition that each sample label must be included in the multiple labels of reference vectors. In our experiments, we firstly evaluate effects of initialization of reference vectors. On the basis of this results, we then evaluate a supervised SOM with k-nearest neighbors by using three kinds of benchmark document data sets. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | self-organizing map / supervised learning / k-nearest neighbors |
Paper # | NC2003-142 |
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Committee | NC |
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Conference Date | 2004/3/10(1days) |
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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) | Learning Model of SOM Using Supervised Information |
Sub Title (in English) | |
Keyword(1) | self-organizing map |
Keyword(2) | supervised learning |
Keyword(3) | k-nearest neighbors |
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 | Shinichi MATSUO |
3rd Author's Affiliation | Kyushu Institute of Technology |
4th Author's Name | Masumi ISHIKAWA |
4th Author's Affiliation | Kyushu Institute of Technology |
Date | 2004/3/10 |
Paper # | NC2003-142 |
Volume (vol) | vol.103 |
Number (no) | 732 |
Page | pp.pp.- |
#Pages | 6 |
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