Reversible Jump MCMC Simulated Annealing for Neural Networks

Reversible Jump MCMC Simulated Annealing for Neural Networks

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...

書誌詳細
主要な著者:	Andrieu, C, de Freitas, N, Doucet, A
フォーマット:	Conference item
出版事項:	Morgan Kaufmann 2000

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