Monte Carlo Theory as an Explanation of Bagging and Boosting

Esposito, Roberto; Saitta, Lorenza

Monte Carlo Theory as an Explanation of Bagging and Boosting

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Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 499-503.

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.

Roberto Esposito, and Lorenza Saitta. 2003. Monte Carlo Theory as an Explanation of Bagging and Boosting. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03. IJCAI, 499–503. https://ijcai.org/proceedings/2003/74

@inproceedings{ijcai2003p74,
  title     = {Monte Carlo Theory as an Explanation of Bagging and Boosting},
  author    = {Esposito, Roberto and Saitta, Lorenza},
  booktitle = {Proceedings of the Eighteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-03}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Georg Gottlob},
  pages     = {499--503},
  year      = {2003},
  url       = {https://ijcai.org/proceedings/2003/74},
}

TY  - CPAPER
TI  - Monte Carlo Theory as an Explanation of Bagging and Boosting
AU  - Esposito, Roberto
AU  - Saitta, Lorenza
T2  - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2003
SP  - 499
EP  - 503
UR  - https://ijcai.org/proceedings/2003/74
ER  -