A penalty-function approach for pruning feedforward neural networks.
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.
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Department of Information Systems and Computer Science, National University of Singapore, Kent Ridge, Republic of Singapore.
D000465:Algorithms; D016000:Cluster Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D011337:Probability Learning; D015203:Reproducibility of Results; D012371:Robotics