This paper introduces the Recurrence Surface Approximation, an
inductive learning method based on linear programming that predicts
recurrence times using censored training examples, that is, examples
in which the available training output may be only a lower bound on
the ``right answer.''  This approach is augmented with a feature
selection method that chooses an appropriate feature set within the
context of the linear programming generalizer.  Computational results
in the field of breast cancer prognosis are shown.  
A straightforward translation of the prediction method to an
artificial neural network model is also proposed.