Monte Carlo Theory as an Explanation of Bagging and Boosting

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.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Roberto Esposito, Lorenza Saitta
• Keywords: Ensemble Learning, IJCAI 2003, IJCAI 2007, Margin Improves Performances, Monte Carlo