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  1. B. 理工学域; 数物科学類・物質化学類・機械工学類・フロンティア工学類・電子情報通信学類・地球社会基盤学類・生命理工学類
  2. b 10. 学術雑誌掲載論文
  3. 1.査読済論文(工)

Selection of minimum training sata for generalization and on-line training by multilayer neural networks

http://hdl.handle.net/2297/6788
http://hdl.handle.net/2297/6788
d41c9b08-6187-426a-b631-02d8475454aa
名前 / ファイル ライセンス アクション
TE-PR-NAKAYAMA-K-436.pdf TE-PR-NAKAYAMA-K-436.pdf (745.0 kB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2017-10-03
タイトル
タイトル Selection of minimum training sata for generalization and on-line training by multilayer neural networks
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
著者 Hara, Kazuyuki

× Hara, Kazuyuki

WEKO 9973

Hara, Kazuyuki

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Nakayama, Kenji

× Nakayama, Kenji

WEKO 353
e-Rad 00207945
研究者番号 00207945

Nakayama, Kenji

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提供者所属
内容記述タイプ Other
内容記述 金沢大学理工研究域電子情報学系
書誌情報 IEEE International Conference on Neural Networks - Conference Proceedings

巻 1, p. 436-441, 発行日 1996-06-01
ISSN
収録物識別子タイプ ISSN
収録物識別子 1098-7576
出版者
出版者 IEEE(Institute of Electrical and Electronics Engineers)
抄録
内容記述タイプ Abstract
内容記述 A training data reduction method for a multilayer neural network (MLNN) is proposed in this paper. This method reduce the data by selecting the minimum number of training data that guarantee generality of the MLNN. For this purpose, two methods are used. One of them is a pairing method which selects the training data by finding the nearest data of the different classes. Data along the class boundary in data space can be selected. The other method is a training method, which used a semi-optimum MLNN in a training process. Since the MLNN classify data based on the distance from the network boundary, the selected data can locate close to the class boundary. So, if the semi-optimum MLNN did not select data from class boundary, pairing method can select them. The proposed methods can be applied to both off-line training and on-line training. The proposed method is also investigated through computer simulation.
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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