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 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.
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Keyword(in English) self-organizing map / supervised learning / k-nearest neighbors
Paper # NC2003-142
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Committee NC
Conference Date 2004/3/10(1days)
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Registration To Neurocomputing (NC)
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
Date of Issue