As a step towards neural realization of various similarity based algorithms k-nn method has been extended to weighted nearest neighbor scheme. Experiments show that for some datasets significant improvements are obtained. As an alternative to the minimization procedures a best-first search weighted nearest neighbor scheme has been implemented. A feature selection method for \knn, based on a variant of the best-first search strategy, has also been implemented. This method is relatively fast and for some databases gives excellent results. Finally a natural neural network extension of k-nn method is described, including weights and other parameters as a part of the model.

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