  Using methods of statistical mechanics, we analyse the effect of outliers on
the supervised learning of a classification problem. The learning strategy aims
at selecting informative examples and discarding outliers. We compare two
algorithms which perform the selection either in a soft or a hard way. When the
fraction of outliers grows large, the estimation errors undergo a first order
phase transition.
