| Abstract |
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A simple algorithm for pruning feedforward neural networks with a single hidden layer trained for function approximation is presented. The algorithm assumes that the net works have been trained with more then the necessary number of hidden units and it consists of two stages. In the first stage, redundant hidden units are removed and in the second stage, irrelevant input units are removed. Experimental results on seven publicly available data sets show that the proposed algorithm outperforms other methods such as nearest neighbor-, decision tree- and regression-based methods.
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 Additional Information
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Citation:
Rudy Setiono, Adam Gaweda,
"Neural Network Pruning for Function Approximation,"
ijcnn,
p. 6443,
IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6,
2000
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