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An efficient EM-based training algorithm for feedforward neural networks
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DOI：10.1016/S0893-6080(96)00049-4.png)
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A fast training algorithm is developed for two-layer feedforward neural networks based on a probabilistic model for hidden representations and the EM algorithm. The algorithm decomposes training the original two-layer networks into training a set of single neurons. The individual neurons are then trained via a linear weighted regression algorithm. Significant improvement on training speed has been made using this algorithm for several bench-mark problems. (C) 1997 Elsevier Science Ltd. All Rights Reserved.
Keyword:
EM algorithm
feedforward networks
fast training
probability model of feedforward networks
hidden representations
linear weighted regression
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