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タイトル: A training data selection in on-line training for multilayer neural networks
著者: Hara, Kazuyuki
Nakayama, Kenji link image
中山, 謙二
発行日: 1998年 5月
出版社(者): Institute of Electrical and Electronics Engineers (IEEE)
引用: IEEE International Conference on Neural Networks - Conference Proceedings 3, pp. 2247-2252
雑誌名: IEEE International Conference on Neural Networks - Conference Proceedings
ISSN: 1098-7576
巻: 34
開始ページ: 2247
終了ページ: 2252
抄録: In this paper, a training data selection method for multilayer neural networks (MLNNs) in on-line training is proposed. Purpose of the reduction in training data is reducing the computation complexity of the training and saving the memory to store the data without losing generalization performance. This method uses a pairing method, which selects the nearest neighbor data by finding the nearest data in the different classes. The network is trained by the selected data. Since the selected data located along data class boundary, the trained network can guarantee generalization performance. Efficiency of this method for the on-line training is evaluated by computer simulation.
URI: http://hdl.handle.net/2297/6887
資料種別: Conference Paper
版表示: publisher

このアイテムを引用あるいはリンクする場合は次の識別子を使用してください。 http://hdl.handle.net/2297/6887



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