Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99162
Title: A penalty-function approach for pruning feedforward neural networks
Authors: Setiono, R. 
Issue Date: 1-Jan-1997
Citation: Setiono, R. (1997-01-01). A penalty-function approach for pruning feedforward neural networks. Neural Computation 9 (1) : 185-204. ScholarBank@NUS Repository.
Abstract: This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/99162
ISSN: 08997667
Appears in Collections:Staff Publications

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