1. A method for classifying an anomaly in a digital image, the method comprising: receiving training data comprising a training feature value for each of a plurality of classification features for each of a plurality of training cases; defining a neighborhood size for each of a plurality of representation points for each classification feature based on the training data; receiving measured data comprising a measured feature value at an evaluation point for each classification feature for a measured case; determining a scale parameter vector for at least some of the representation points near the evaluation point for each classification feature using the neighborhood size data for that classification feature; determining a weight factor for at least some of the representation points using the respective scale parameter vector; and applying the weight factor for at least some of the representation points to the training data at the plurality of representation points to generate a classification probability for the measured case at the evaluation point.