Monte Carlo theory as an explanation of bagging and boosting
Roberto Esposito, Lorenza Saitta · 2003
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. 1