Nikunj C. Oza's Publications

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Online Bagging and Boosting

Online Bagging and Boosting. Nikunj C. Oza. In International Conference on Systems, Man, and Cybernetics, Special Session on Ensemble Methods for Extreme Environments, pp. 2340–2345, Institute for Electrical and Electronics Engineers, New Jersey, October 2005.

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Abstract

Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.

BibTeX Entry

@inproceedings{oza05,
	author={Nikunj C. Oza},
	title={Online Bagging and Boosting},
	booktitle={International Conference on Systems, Man, and Cybernetics, Special Session on Ensemble Methods for Extreme Environments}, 
	publisher={Institute for Electrical and Electronics Engineers},
	address={New Jersey},
	editor={Mo Jamshidi},
	pages={2340-2345},
	month={October},
abstract={Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.},
	bib2html_pubtype={Refereed Conference},
	bib2html_rescat={Ensemble Learning},
	year={2005}
}

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