Unsupervised Learning of 3D Objects Conserving Global Topological Order

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概要

• 論文の詳細を見る

• The self-organization rule of planar neural networks has been proposed for learning of 2D distributions. However, it cannot be applied to 3D objects. In this paper, we propose a new model for global representation of the 3D objects. Based on this model, global topology reserving self-organization is achieved using parallel local competitive learning rule such as Kohonen's maps. The proposed model is able to represent the objects distributively and easily accommodate local features.

• 社団法人電子情報通信学会の論文
• 1993-05-25

著者

• Minowa Kenji

  The Faculty Of Engineering Tokyo Institute Of Technology

• Chao Jinhui

  The Faculty Of Science And Engineering Chou University

• Tsujii Shigeo

  the Faculty of Engineering, Tokyo Institute of Technology

• Tsujii Shigeo

  The Faculty Of Engineering Tokyo Institute Of Technology

関連論文

• Unsupervised Learning of 3D Objects Conserving Global Topological Order
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