# Optimal Linear Combination of Classifiers

arXiv: https://arxiv.org/abs/2103.01109 · HTML: https://mlsanity.com/paper/2103.01109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [A Note on Posterior Probability Estimation for Classifiers](https://mlsanity.com/paper/1909.05894/) (2019)

## Abstract (arXiv)

The question of whether to use one classifier or a combination of classifiers is a central topic in Machine Learning. We propose here a method for finding an optimal linear combination of classifiers derived from a bias-variance framework for the classification task.