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Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL

, : Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. Neural Networks 11(1): 89-116

Predicting conditional probability densities with neural networks requires complex (at least two-hidden-layer) architectures, which normally leads to rather long training times. By adopting the RVFL concept and constraining a subset of the parameters to randomly chosen initial values (such that the EM-algorithm can be applied), the training process can be accelerated by about two orders of magnitude. This allows training of a whole ensemble of networks at the same computational costs as would be required otherwise for training a single model. The simulations performed suggest that in this way a significant improvement of the generalization performance can be achieved. Copyright 1997 Elsevier Science Ltd.


PMID: 12662851

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