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Abstract: Training neural networks for predicting conditional probability densities can be accelerated considerably by adopting the random vector functional link net (RVFL) approach. In this way, a whole ensemble of models can be trained at the same computational costs as otherwise required for training only one conventional network. Simulations on a synthetic time series and a real-world benchmark problem suggest that a considerable improvement in the generalisation performance over that of a... (Update)
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BibTeX entry: (Update)
A. K. Husmeier D., Modelling conditional probabilities with network committees: how overfitting can be useful, Neural Network World 8 (1998) 417-- 439. http://citeseer.ist.psu.edu/article/husmeier98modelling.html More
@misc{ husmeier98modelling,
author = "A. Husmeier",
title = "Modelling conditional probabilities with network committees: how overfitting
can be useful",
text = "A. K. Husmeier D., Modelling conditional probabilities with network committees:
how overfitting can be useful, Neural Network World 8 (1998) 417-- 439.",
year = "1998",
url = "citeseer.ist.psu.edu/article/husmeier98modelling.html" }
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