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アイテム
Learning Initialized by Topologically Correct Map
http://hdl.handle.net/10445/5285
http://hdl.handle.net/10445/52854e34f117-9291-4225-baed-04878838bcc5
| 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 | Learning Initialized by Topologically Correct 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 | |||||
| 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× Trappenberg, Thomas |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this research, we proposed a model of hierarchical three-layered perceptron, in which the middle layer contains a two dimensional map where the topological relationship of the high dimensional input data (external world) are internally representated. The proposed model executes a two-phase learning algorithm, such taht a supervised learning is proceded by a self-organization unsupervised learning. The objective of this study is to build a simple neural network model (which is more biologicaly realistic than the standard Multilayer Perceptron model), that can form an internal representation that supports its learning potential. | |||||
| 書誌情報 |
Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2009) p. 2802-2806, 発行日 2009 |
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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 | |||||
