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, provides a new notion of learner instability, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.
Monte Carlo Theory as an Explanation of Bagging and Boosting
ESPOSITO, Roberto;
2003-01-01
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, provides a new notion of learner instability, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.
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