---
title: "Monte Carlo Theory as an Explanation of Bagging and Boosting"
authors:
  - "Roberto Esposito"
  - "Lorenza Saitta"
doi: ""
url: "https://ijcai.org/proceedings/2003/74"
pdf: "https://ijcai.org/proceedings/2003/0074.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "499-503"
track: ""
keywords:
---

# Monte Carlo Theory as an Explanation of Bagging and Boosting

Roberto Esposito, Lorenza Saitta

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 499-503.

## Abstract

In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will be useful, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.