Monte Carlo Theory as an Explanation of Bagging and Boosting
نویسندگان
چکیده
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 wil l be useful, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.
منابع مشابه
Importance Sampled Learning Ensembles
Learning a function of many arguments is viewed from the perspective of high– dimensional numerical quadrature. It is shown that many of the popular ensemble learning procedures can be cast in this framework. In particular randomized methods, including bagging and random forests, are seen to correspond to random Monte Carlo integration methods each based on particular importance sampling strate...
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