  The maximum entropy principle advocates to evaluate events' probabilities
using a distribution that maximizes entropy among those that satisfy certain
expectations' constraints. Such principle can be generalized for arbitrary
decision problems where it corresponds to minimax approaches. This paper
establishes a framework for supervised classification based on the generalized
maximum entropy principle that leads to minimax risk classifiers (MRCs). We
develop learning techniques that determine MRCs for general entropy functions
and provide performance guarantees by means of convex optimization. In
addition, we describe the relationship of the presented techniques with
existing classification methods, and quantify MRCs performance in comparison
with the proposed bounds and conventional methods.
