Abstract
The problem of reducing the size of a trained multilayer artificial neural network is addressed, and a method of removing hidden units is developed. The method is based on the idea of eliminating units and adjusting remaining weights in such a way that the network performance does not worsen over the entire training set. The pruning problem is formulated in terms of a system of linear equations, and a very efficient conjugate-gradient algorithm is used for solving it, in the least squares sense. The algorithm also provides a sub-optimal criterion for choosing the units to be removed, which is proved to work well in practice. Preliminary results over a simulated pattern recognition task are reported, which demonstrate the effectiveness of the proposed approach.
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© 1993 Springer-Verlag Berlin Heidelberg
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Pelillo, M., Fanelli, A.M. (1993). A method of pruning layered feed-forward neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_160
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DOI: https://doi.org/10.1007/3-540-56798-4_160
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