In this paper we propose the use of the framework of Monte Carlo stochastic algorithms to analyze ensemble learning, specifically, bagging. In particular, this framework allows one to explain baggingrsquos behavior and also why increasing the margin improves performances. Experimental results support the theoretical analysis.
Explaining Bagging with Monte Carlo Theory
ESPOSITO, Roberto;
2003-01-01
Abstract
In this paper we propose the use of the framework of Monte Carlo stochastic algorithms to analyze ensemble learning, specifically, bagging. In particular, this framework allows one to explain baggingrsquos behavior and also why increasing the margin improves performances. Experimental results support the theoretical analysis.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
