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Conference Paper

A practical Monte Carlo implementation of Bayesian learning

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rasmussen, C. (1996). A practical Monte Carlo implementation of Bayesian learning. Advances in Neural Processing Systems 8, 598-604.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-EB64-D
Abstract
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.