A Structure Trainable Neural Network with Embedded Gating Units and Its Learning Algorithm

A Structure Trainable Neural Network with Embedded Gating Units and Its Learning Algorithm

フォーマット:

論文

責任表示:

Nakayama, Kenji ; Hirano, Akihiro ; Kanbe, Aki

言語:

英語

出版情報:

IEEE(Institute of Electrical and Electronics Engineers), 2000-07-01

著者名:

掲載情報:

Proceedings of the International Joint Conference on Neural Networks

巻:

3

開始ページ:

III-253

終了ページ:

III-258

バージョン:

publisher

概要:

Many problems solved by multilayer neural networks (MLNNs) are reduced into pattern mapping. If the mapping includes several different rules, it is difficult to solve these problems by using a single MLNN with linear connection weights and continuous activation functions. In this paper, a structure trainable neural network has been proposed. The gate units are embedded, which can be trained together with the connection weights. Pattern mapping problems, … which include several different mapping rules, can be realized using a single new network. Since, some parts of the network can be commonly used for different mapping rules, the network size can be reduced compared with the modular neural networks, which consists of several independent expert networks. 続きを見る

URL:

http://hdl.handle.net/2297/6825

タイトル・著者・出版者が同じ資料

詳細

主題:	Learning systems; Multilayer neural networks; Pattern mapping; Pattern recognition
受理日:	2000-07-01

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