On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Peter Sykacek


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 a-posteriori probabilities of dif(cid:173) ferent input subsets.