Work

An optimal aggregation type classifier

Year: 2014

Type: preprint

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 $M$ 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 si\-mu\-lation are reported both, for high dimensional and functional data.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1411.2687

Authors: Cholaquidis, Alejandro, Fraiman, Ricardo, Kalemkerian, Juan, Llop, Pamela

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2142907149

Cites: 8

Cited by: 1

Related to: 10