  Usually standard algorithms employ a loss where each error is the mere
absolute difference between the true value and the prediction, in case of a
regression task. In the present, we introduce several error weighting schemes
that are a generalization of the consolidated routine. We study both a binary
classification model for Support Vector Classification and a regression net for
Multi-layer Perceptron. Results proves that the error is never worse than the
standard procedure and several times it is better.
