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The task of RBF kernel selection in Relevance Vector Machines (RVM) is considered. RVM exploits a probabilistic Bayesian learning framework offering number of advantages to state-of-the-art Support Vector Machines. In particular RVM effectively avoids determination of regularization coefficient C via evidence maximization. In the paper we show that RBF kernel selection in Bayesian framework requires...