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Abstract: We present a Bayesian framework for inferring the parameters of a mixture of experts model based on ensemble learning by variational free energy minimisation. The Bayesian approach avoids the over-fitting and noise level under-estimation problems of traditional maximum likelihood inference. We demonstrate these methods on artificial problems and sunspot time series prediction. INTRODUCTION The task of estimating the parameters of adaptive models such as artificial neural networks using Maximum... (Update)
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BibTeX entry: (Update)
Waterhouse, S., MacKay, D.J.C. & Robinson, T. (1996). Bayesian methods for mixtures of experts. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press. http://citeseer.ist.psu.edu/waterhouse96bayesian.html More
@inproceedings{ waterhouse96bayesian,
author = "Steve Waterhouse and David MacKay and Tony Robinson",
title = "Bayesian Methods for Mixtures of Experts",
booktitle = "Advances in Neural Information Processing Systems",
volume = "8",
publisher = "The {MIT} Press",
editor = "David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo",
pages = "351--357",
year = "1996",
url = "citeseer.ist.psu.edu/waterhouse96bayesian.html" }
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301 Neural networks and the bias / variance dilemma (context) - Geman, Bienenstock et al. - 1992
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122 Predicting the future: a connectionist approach (context) - Weigend, Huberman et al. - 1990
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75 The evidence framework applied to classification networks - MacKay
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