  A main property of support vector machines consists in the fact that only a
small portion of the training data is significant to determine the maximum
margin separating hyperplane in the feature space, the so called support
vectors. In a similar way, in the general scheme of learning from constraints,
where possibly several constraints are considered, some of them may turn out to
be unnecessary with respect to the learning optimization, even if they are
active for a given optimal solution. In this paper we extend the definition of
support vector to support constraint and we provide some criteria to determine
which constraints can be removed from the learning problem still yielding the
same optimal solutions. In particular, we discuss the case of logical
constraints expressed by Lukasiewicz logic, where both inferential and
algebraic arguments can be considered. Some theoretical results that
characterize the concept of unnecessary constraint are proved and explained by
means of examples.
