WEKO3
アイテム
Extraction of Embedded Hierarchical Structure of Knowledge from Trained Boltzmann Machine by Genetic algorithm
http://hdl.handle.net/10445/6210
http://hdl.handle.net/10445/6210204919dc-81b6-45a7-b14b-ef69d1aa34dc
| 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 | Extraction of Embedded Hierarchical Structure of Knowledge from Trained Boltzmann Machine by Genetic algorithm | |||||
| 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 |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This study focuses on knowledge representa-tion as based on the distributed representation by the Boltzmann machine. In this type of framework, concepts are represented uniformly without any information on their structural relationship. To utilize such information, this study explores how the hierarchical structure of concepts can be extracted from the weights of a trained network. The hierarchy dealt with here is a taxonomical one which is represented by a tree of microfeatures of a concept. Here, the tree is extracted by using various kinds of local criteria relating to weight distribution. To satisfy them simultaneously, a Genetic Algorithm is adopted. | |||||
| 書誌情報 |
Proceedings of the Artificial Neural Networks In Engineering 発行日 1994-11 |
|||||
| 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 | |||||
