Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals

Many connectionist learning models are implemented using a gradient descent in a least squares error function of the output and teacher signal. For small r's a "city-block" error metric is approximated and for large r's the "maximum" or "supremum" metric is approached. An implementation of Minkowski-r back-propagation is described. Different r values may be appropriate for the reduction of the effects of outliers (noise).