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IJCAI
2003

Monte Carlo Theory as an Explanation of Bagging and Boosting

11 years 1 months ago
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.
Roberto Esposito, Lorenza Saitta
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Roberto Esposito, Lorenza Saitta
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