Monte Carlo theory as an explanation of bagging and boosting

Authors:
Roberto Esposito;Lorenza Saitta
Affiliations:
Universita di Torino, Torino, Italy;Universita del Piemonte Orientale, Alessandria, Italy
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

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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.