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Learning Prototypes and Distances: a prototype reduction technique based on nearest neighbor error minimization. Pattern Recognition, 2006. Vol. 39 (2), pp. 180-188.A prototype reduction algorithm is proposed which simultaneously trains both a reduced set of prototypes and a suitable local metric for these prototypes. Starting with an initial selection of a small number of prototypes, it iteratively adjusts both the position (features) of these prototypes and the corresponding local-metric weights. The resulting prototypes/metric combination minimizes a suitable estimation of the classification error probability. Good performance of this algorithm is assessed through experiments with a number of benchmark data sets and with a real task consisting in the verification of images of human faces.