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Reversible Jump MCMC Simulated Annealing for Neural Networks

Abstract:

We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the classical...

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Publisher:
Morgan Kaufmann
Host title:
Uncertainty in Artificial Intelligence (UAI)
Publication date:
2000-01-01
UUID:
uuid:f5d3e741-d2f3-4a00-ae69-85bd94ddd0c2
Local pid:
cs:7540
Deposit date:
2015-03-31

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