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Representing Taxonomical Hierarchy of Knowledge by Structured Boltzmann Machine
http://hdl.handle.net/10445/6211
http://hdl.handle.net/10445/6211fd4354d4-9ca1-478c-a9fe-516cc1338e2f
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2011-04-19 | |||||
| タイトル | ||||||
| タイトル | Representing Taxonomical Hierarchy of Knowledge by Structured Boltzmann Machine | |||||
| 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 |
奥野, 拓
× 奥野, 拓× Kakazu, Yukinori |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The Boltzmann machine for content address-able memory is structured to explicitly deal with taxonomical hierarchy of learned concepts embedded in its weights. It is realized by iterating three processes: extracting a sub-network which represents an abstract concept, replacing it with a unit, and generating new layer by connecting it with the subnetwork. By this architecture, constraints on such hierarchy embedded in knowledge can be utilized to process knowledge to some extent. | |||||
| 書誌情報 |
Proceedings of the IEEE World Congress on Computational Intelligence 発行日 1994-11 |
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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 | |||||
