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Fast backpropagation for supervised learning
1993 / IEEE / 0-7803-1421-2
This item was taken from the IEEE Periodical ' Fast backpropagation for supervised learning ' In this paper, fast backpropagation (Fbp), a new, simple and computationally efficient variant of the standard backpropagation, is proposed. It continuously adapts the learning rate parameter /spl epsiv/, for individual synapses, using only network variables, without any significant increase in circuit complexity. The method is related to Fermi-Dirac distribution which is based upon quantum principles. The 'mean' update procedure employed offers a fascinating degree of stability and robustness. Even on individual runs Fbp, on average, converges quicker, particularly for non-Boolean inputs, and generalizes better than Quickprop with an identical set of initial random weights.
Learning Rate Parameter
Learning (artificial Intelligence)
Mean Update Procedure