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 data-limited tasks from real world domains.
Advances in Neural Information Processing Systems 8, eds. D. S. Touretzky, M. C. Mozer, M. E. Hasselmo, MIT Press, 1996, pp. 598-604
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