  We introduce a nonlinear aggregation type classifier for functional data
defined on a separable and complete metric space. The new rule is built up from
a collection of $M$ arbitrary training classifiers. If the classifiers are
consistent, then so is the aggregation rule. Moreover, asymptotically the
aggregation rule behaves as well as the best of the $M$ classifiers. The
results of a small simulation are reported both, for high dimensional and
functional data, and a real data example is analyzed.
