IMPROVING GENERALIZATION OF NEURAL NETWORKS THROUGH PRUNING
- 1 January 1991
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Neural Systems
- Vol. 01 (04) , 317-326
- https://doi.org/10.1142/s0129065791000352
Abstract
A technique for constructing neural network architectures with better ability to generalize is presented under the name Ockham's Razor: several networks are trained and then pruned by removing connections one by one and retraining. The networks which achieve fewest connections generalize best. The method is tested on a classification of bit strings (the contiguity problem): the optimal architecture emerges, resulting in perfect generalization. The internal representation of the network changes substantially during the retraining, and this distinguishes the method from previous pruning studies.Keywords
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