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Class-Proximity SOM and its Applications in Classification
http://hdl.handle.net/10445/5281
http://hdl.handle.net/10445/52814ba47521-b06c-49fb-8b03-fd7ae89b4cd5
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-11-29 | |||||
| タイトル | ||||||
| タイトル | Class-Proximity SOM and its Applications in Classification | |||||
| 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 | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hartono, Pitoyo
× Hartono, Pitoyo× Saito, Aya |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this study, we propose a model of Self-Organizing Map (SOM) capable of mapping highdimensional data into a low dimension space by preserving not only thefeature-proximity of the original data but also their class-proximity. A conventional SOM is known to map original high dimensional data with similar features into points located close to each other in the low dimensional map in a so called competitive layer. In addition to this feature, the proposed SOM is also able to map high dimensional data belonging to a same class in each other's proximities. These characteristics retains the ability of the map to be used as a visualization tool of high dimensional data while also support the execution of high quality pattern classifications in the low dimensional map. In the experiments the classification performance of the proposed SOM is compared to that of MLP with regards to wide varieties of problems. | |||||
| 書誌情報 |
Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008) p. 2150-2155, 発行日 2008 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 国際会議/International Conference | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| 出版者 | ||||||
| 出版者 | IEEE | |||||
