Before revision:  We apply the model to the subset of numbers "0" and "1" of the classical MNIST database, where we are able to discern individuals from both classes with more than 94\\%DIF < accuracy, sometimes using less than 80 variables, a feature that is out of reach of Artificial Neural Networks without weight sharing or feature engineering.
After revision:  We illustrate the classifier's qualities by applying it to the subset of numbers "0" and "1" of the classical MNIST database, where we are able to discern individuals from both classes with more than 94\\%DIF < accuracy, sometimes using less than 80 variables, a feature that is out of reach of Artificial Neural Networks without weight sharing or feature engineering.