A Training Method with Small Computation for Classification

A Training Method with Small Computation for Classification

フォーマット:

論文

責任表示:

Hara, Kazuyuki ; Nakayama, Kenji

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

開始ページ:

III-543

終了ページ:

III-548

バージョン:

publisher

概要:

A training data selection method for multi-class data is proposed. This method can be used for multilayer neural networks (MLNN). The MLNN can be applied to pattern classification, signal process, and other problems that can be considered as the classification problem. The proposed data selection algorithm selects the important data to achieve a good classification performance. However, the training using the selected data converges slowly, so we also … propose an acceleration method. The proposed training method adds the randomly selected data to the boundary data. The validity of the proposed methods is confirmed through the computer simulation. 続きを見る

URL:

http://hdl.handle.net/2297/6823

タイトル・著者・出版者が同じ資料

詳細

主題:	Computer simulation; Data selection method; Learning algorithms; Learning systems; Multilayer neural networks; Pattern recognition
受理日:	2000-07-01

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