Robust full Bayesian learning for radial basis networks.
We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to pe...
| Auteurs principaux: | , , |
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| Format: | Journal article |
| Langue: | English |
| Publié: |
2001
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