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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/1179

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Title: A Training Scheme for Pattern Classification Using Multi-layer Feed-forward Neural Networks.
Authors: Keeni, Kanad
Nakayama, Kenji
Shimodaira, Hiroshi
Issue Date: 1999
Citation: In IEEE International Conference on Computational Intelligence and Multimedia Applications, pages 307-311, Sep 1999.
Publisher: IEEE
Abstract: This study highlights on the subject of weight initialization in multi-layer feed-forward networks. Training data is analyzed and the notion of criti- cal point is introduced for determining the initial weights for the input to hidden layer synaptic con- nections. The proposed method has been applied to artificial data. The experimental results show that the proposed method takes almost 1/2 of the train- ing time required for standard back propagation.
URI: http://hdl.handle.net/1842/1179
Appears in Collections:CSTR publications

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