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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/2297/6788d41c9b08-6187-426a-b631-02d8475454aa
名前 / ファイル | ライセンス | アクション |
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TE-PR-NAKAYAMA-K-436.pdf (745.0 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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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× 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 |
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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 |