  We address the issue of binary classification in Banach spaces in presence of
uncertainty. We show that a number of results from classical support vector
machines theory can be appropriately generalised to their robust counterpart in
Banach spaces. These include the Representer Theorem, strong duality for the
associated Optimization problem as well as their geometric interpretation.
Furthermore, we propose a game theoretic interpretation by expressing a Nash
equilibrium problem formulation for the more general problem of finding the
closest points in two closed convex sets when the underlying space is reflexive
and smooth.
