Presentation | 1997/3/18 Study of a classification method by self-organizing feature map algorithm Mizue KAYAMA, Toshio OKAMOTO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The main purpose of this research is to improve the classification performance of the competitive learning on a neural network. To fulfill this purpose, a classification algorithm was proposed, and a classifying system embedded the peculiar algorithm has been developed. We conducted the experiments for checking the validity of the result obtained from our system. In this paper, we propose the classification algorithm which has robustness to classification accuracy for noise. In order to guarantee the power mentioned above, we contrive the mechanism which decide boundary lines in competitive layer of neural network based on each weight value of all units. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Neural network / Classification / Learning for neighborhood units / Self-organizing feature map |
Paper # | AI96-43,KBSE96-33 |
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Conference Information | |
Committee | AI |
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Conference Date | 1997/3/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Study of a classification method by self-organizing feature map algorithm |
Sub Title (in English) | |
Keyword(1) | Neural network |
Keyword(2) | Classification |
Keyword(3) | Learning for neighborhood units |
Keyword(4) | Self-organizing feature map |
1st Author's Name | Mizue KAYAMA |
1st Author's Affiliation | The Graduate School of Information Systems,University of Electro-Communications() |
2nd Author's Name | Toshio OKAMOTO |
2nd Author's Affiliation | The Graduate School of Information Systems,University of Electro-Communications |
Date | 1997/3/18 |
Paper # | AI96-43,KBSE96-33 |
Volume (vol) | vol.96 |
Number (no) | 594 |
Page | pp.pp.- |
#Pages | 8 |
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