ML Anthology

Monte Carlo Theory as an Explanation of Bagging and Boosting

Roberto Esposito, Lorenza Saitta
IJCAI 2003 pp. 499-504
/ijcai/2003/esposito2003ijcai-monte/

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. 1

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Text

Esposito and Saitta. "Monte Carlo Theory as an Explanation of Bagging and Boosting." International Joint Conference on Artificial Intelligence, 2003.

Markdown

[Esposito and Saitta. "Monte Carlo Theory as an Explanation of Bagging and Boosting." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/esposito2003ijcai-monte/)

BibTeX

@inproceedings{esposito2003ijcai-monte,
  title     = {{Monte Carlo Theory as an Explanation of Bagging and Boosting}},
  author    = {Esposito, Roberto and Saitta, Lorenza},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2003},
  pages     = {499-504},
  url       = {https://mlanthology.org/ijcai/2003/esposito2003ijcai-monte/}
}