Kagan Tumer's Publications

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Classifier Combining: Analytical Results and Implications. K. Tumer and J. Ghosh. In Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, workshop at the Thirteenth National Conference on Artificial Intelligence, Portland, OR, August 1996.

Abstract

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This paper summarizes our recent theoretical results that quantify the improvements due to multiple classifier combining. Furthermore, we present an extension of this theory that leads to an estimate of the Bayes error rate. Practical aspects such as expressing the confidences in decisions and determining the best data partition/classifier selection are also discussed.

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BibTeX Entry

@inproceedings{tumer-ghosh_imlm96,
        author={K. Tumer and J. Ghosh},
        title={Classifier Combining: Analytical Results and Implications},
        booktitle= {Integrating Multiple Learned Models for Improving and
		Scaling Machine Learning Algorithms, workshop at the 
		Thirteenth National Conference on Artificial Intelligence},
        address="Portland, OR",
	month ={August},
	abstract={Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This paper summarizes our recent theoretical results that quantify the improvements due to multiple classifier combining. Furthermore, we present an extension of this theory that leads to an estimate of the Bayes error rate. Practical aspects such as expressing the confidences in decisions and determining the best data partition/classifier selection are also discussed.},
	bib2html_pubtype = {Workshop/Symposium Papers},
	bib2html_rescat = {Classifier Ensembles, Bayes Error Estimation},
        year={1996}
}

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