title: Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL.
abstract: 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.
authors.affiliation: Department of Mathematics, King's College London, UK
authors.forename: John G.
authors.initials: JG
authors.lastname: Taylor
authors.forename: Dirk
authors.initials: D
authors.lastname: Husmeier