A Training Scheme for Pattern Classification Using Multi-layer Feed-forward Neural Networks.
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Date
1999Author
Keeni, Kanad
Nakayama, Kenji
Shimodaira, Hiroshi
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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.