An optimal 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 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.Fil: Cholaquidis, Alejandro. Universidad de la República. Facultad de Ciencias; UruguayFil: Kalemkerian, Juan. Universidad de la República. Facultad de Ciencias; UruguayFil: Fraiman, Ricardo. Universidad de la República. Facultad de Ciencias; UruguayFil: Llop Orzan, Pamela Nerina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentin