# A nonlinear aggregation type classifier

arXiv: https://arxiv.org/abs/1509.01604 · HTML: https://mlsanity.com/paper/1509.01604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [GROS: A General Robust Aggregation Strategy](https://mlsanity.com/paper/2402.15442/) (2024)

## Abstract (arXiv)

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.