WEKO3
アイテム
Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures
http://hdl.handle.net/2261/00074081
http://hdl.handle.net/2261/0007408147c1f32b-a881-47b2-a2f2-8e0a2a6cf6ae
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures 2000.pdf (358.2 kB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-12-14 | |||||
| タイトル | ||||||
| タイトル | Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | journal article | |||||
| 著者 |
IMAI, Hiroshi
× IMAI, Hiroshi× INABA, Mary |
|||||
| 著者所属 | ||||||
| 著者所属 | Department of Information Science, University of Tokyo | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This paper surveys recent progress in the investigation of the underlying discrete proximity structures of geometric clustering with respect to the divergence in information geometry. Geometric clustering with respect to the divergence provides powerful unsupervised learning algorithms, and can be applied to classifying and obtaining generalizations of complex objects represented in the feature space. The proximity relation, defined by the Voronoi diagram by the divergence, plays an important role in the design and analysis of such algorithms. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | INVITED PAPER (Special Issue on Surveys on Discovery Science) | |||||
| 書誌情報 |
IEICE TRANSACTIONS on Information and Systems 巻 E83-D, 号 1, p. 27-35, 発行日 2000-01-25 |
|||||
| 権利 | ||||||
| 権利情報 | copyright©2000 IEICE | |||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
| 出版者 | ||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||
| 関係URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://search.ieice.org/ | |||||
