An optimal aggregation type classifier
Statistics Theory
2014-11-12 v1 Statistics Theory
Abstract
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 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 classifiers. The results of a small si\-mu\-lation are reported both, for high dimensional and functional data.
Cite
@article{arxiv.1411.2687,
title = {An optimal aggregation type classifier},
author = {Alejandro Cholaquidis and Ricardo Fraiman and Juan Kalemkerian and Pamela Llop},
journal= {arXiv preprint arXiv:1411.2687},
year = {2014}
}