A probabilistic k-nn (PKnn) method was introduced in  under the Bayesian point of view. This work showed that posterior inference over the parameter
can be performed in a relatively straightforward manner using Markov Chain Monte Carlo (MCMC) methods. This method was extended by Everson and Fieldsen  to deal with metric learning. In this work we propose two different dissimilarities functions to be used inside this PKnn framework. These dissimilarities functions can be seen as a simplified version of the full-covariance distance functions just proposed. Furthermore we propose to use a class- dependent dissimilarity function as proposed in  aim at improving the k-nn classifier. In the present work we pursue a simultaneously learning of the dissimilarity function parameters together with the parameter
of the k-nn classifier. The experiments show that this simultaneous learning lead to an improvement of the classifier with respect to the standard k-nn and state-of-the-art technique as well.