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Online Bagging and Boosting. Nikunj C. Oza and Stuart Russell. In Eighth International Workshop on Artificial Intelligence and Statistics, pp. 105–112, Morgan Kaufmann, Key West, Florida. USA, January 2001.
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 performed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts.
@inproceedings{ozru01a,
author={Nikunj C. Oza and Stuart Russell},
title={Online Bagging and Boosting},
booktitle={Eighth International Workshop on Artificial Intelligence and Statistics},
publisher={Morgan Kaufmann},
month={January},
address={Key West, Florida. USA},
editor={Tommi Jaakkola and Thomas Richardson},
pages={105-112},
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 performed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Ensemble Learning},
year = {2001}
}
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