  We investigate whether it is possible to learn rule sets efficiently in a
network structure with a single hidden layer using iterative refinements over
mini-batches of examples. A first rudimentary version shows an acceptable
performance on all but one dataset, even though it does not yet reach the
performance levels of Ripper.
