Online Bagging and Boosting

Nikunj C. Oza, Stuart J. Russell
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:229-236, 2001.

Abstract

Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, and no effective online versions have been proposed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts.

Cite this Paper


BibTeX
@InProceedings{pmlr-vR3-oza01a, title = {Online Bagging and Boosting}, author = {Oza, Nikunj C. and Russell, Stuart J.}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {229--236}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/oza01a/oza01a.pdf}, url = {http://proceedings.mlr.press/r3/oza01a.html}, abstract = {Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, and no effective online versions have been proposed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Online Bagging and Boosting %A Nikunj C. Oza %A Stuart J. Russell %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-oza01a %I PMLR %P 229--236 %U http://proceedings.mlr.press/r3/oza01a.html %V R3 %X Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, and no effective online versions have been proposed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts. %Z Reissued by PMLR on 31 March 2021.
APA
Oza, N.C. & Russell, S.J.. (2001). Online Bagging and Boosting. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:229-236 Available from http://proceedings.mlr.press/r3/oza01a.html. Reissued by PMLR on 31 March 2021.

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