Online Bagging and Boosting
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
Text
Oza and Russell. "Online Bagging and Boosting." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Oza and Russell. "Online Bagging and Boosting." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/oza2001aistats-online/)BibTeX
@inproceedings{oza2001aistats-online,
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},
year = {2001},
pages = {229-236},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/oza2001aistats-online/}
}