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Abstract:
Bayesian methods have been successfully applied to regression
and classification problems in multi-layer perceptrons. We present
a novel application of Bayesian techniques to Radial Basis Function
networks by developing a Gaussian approximation to the posterior
distribution which, for fixed basis function widths, is analytic in
the parameters. The setting of regularization constants by
cross-validation is wasteful as only a single optimal parameter
estimate is retained. We treat this issue in a principled manner by
assigning prior distributions to these constants, which are then
adapted in light of the data under a simple re-estimation
formula.
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