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In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification. We present an analysis valid in(More)
We introduce a novel Markov chain Monte Carlo algorithm for estimation of posterior probabilities over discrete model spaces. Our learning approach is applicable to families of models for which the marginal likelihood can be analytically calculated, either exactly or approximately, given any fixed structure. It is argued that for certain model neighborhood(More)
In present paper we study the use of the expectation maximization (EM) algorithm in classiication. The EM-algorithm is used to calculate the probability of each vector belonging to each class. If we assign each vector to the class of maximal probability we get a classiication minimizing a certain log-likelihood function. By analyzing these probabilities we(More)