A nonlinear aggregation type classifier

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 MM 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 MM classifiers. The results of a small simulation are reported both, for high dimensional and functional data, and a real data example is analyzed.