DocumentCode
346155
Title
A training scheme for pattern classification using multi-layer feed-forward neural networks
Author
Keeni, Kanad ; Nakayama, Kenji ; Shimodaira, Hiroshi
Author_Institution
Dept. of Inf. Syst. & Quantitative Sci., Nanzan Univ., Japan
fYear
1999
fDate
1999
Firstpage
307
Lastpage
311
Abstract
This study highlights the subject of weight initialization in multi-layer feed-forward networks. Training data is analyzed and the notion of critical point is introduced for determining the initial weights for input to hidden layer synaptic connections. The proposed method has been applied to artificial data. Experimental results show that the proposed method takes almost half the training time required for standard backpropagation
Keywords
backpropagation; feedforward neural nets; multilayer perceptrons; pattern classification; backpropagation; critical point; hidden layer; initial weights; multilayer feedforward neural networks; pattern classification; synaptic connections; training data; training scheme; weight initialization; Feedforward neural networks; Feedforward systems; Multi-layer neural network; Neural networks; Pattern classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Multimedia Applications, 1999. ICCIMA '99. Proceedings. Third International Conference on
Conference_Location
New Delhi
Print_ISBN
0-7695-0300-4
Type
conf
DOI
10.1109/ICCIMA.1999.798548
Filename
798548
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