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Improving generalization of a well trained network

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2 Author(s)
Chakraborty, G. ; Aizu Univ., Fukushima, Japan ; Noguchi, S.

Feedforward neural networks trained with a small set of noisy samples are prone to overtraining and poor generalization. On the other hand, a very small network could not be trained at all because it would be biased by its own architecture. Thus, it is an old problem to ascertain that a well trained network would also deliver good generalization. Theoretical results give bounds on generalization error, but with worst case estimations which is of less practical use. In practice cross-validation is used to estimate generalization. We propose a method to construct network so as to ascertain good generalization, even after sufficient training. Simulations show very good results in support of our algorithm. Some theoretical aspects are discussed

Published in:

Neural Networks, 1996., IEEE International Conference on  (Volume:1 )

Date of Conference:

3-6 Jun 1996