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Abstract:
In this paper we will treat input selection for a radial
basis function (RBF) like classifier within a Bayesian framework.
We approximate the a-posteriori distribution over both model
coefficients and input subsets by samples drawn with Gibbs updates
and reversible jump moves. Using some public datasets we compare
the classification accuracy of the method with a conventional ARD
scheme. These datasets are also used to infer the relevance of
different input subsets.
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