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Topology analysis of data space using self-organizing feature map
http://hdl.handle.net/2297/6782
http://hdl.handle.net/2297/6782e4ef0006-86ef-459b-aa4f-7e83064c2dac
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-789.pdf (289.5 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
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| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | Topology analysis of data space using self-organizing feature map | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Minamimoto, Kazuhiro
× Minamimoto, Kazuhiro× Ikeda, Kazushi× Nakayama, Kenji |
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| 書誌情報 |
IEEE International Conference on Neural Networks - Conference Proceedings 巻 2, p. 789-794, 発行日 1995-11-01 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1098-7576 | |||||
| 出版者 | ||||||
| 出版者 | IEEE(Institute of Electrical and Electronics Engineers) | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In order to analyze the topological structure of the data space using Kohonen's self-organizing feature map (SOFM), a criterion is discussed. The Euclidian distance between the reference vector and the data, the number of the reference vectors and the topology preserving measure are taken into account, and are combined in a unified criterion. Through computer simulation, it is confirmed that goodness of the different reference topologies, that is dimensions, can be clearly discriminated regardless the parameters. Thus, the unified criterion makes it possible to analyze the essential data space topology. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
