Kagan Tumer's Publications

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Classifier Combining through Trimmed Means and Order Statistics. K. Tumer and J. Ghosh. In Proceedings of the International Joint Conference on Neural Networks, pp. 757–762, Anchorage, AL, June 1998.

Abstract

Combining the outputs of multiple neural networks has led to substantial improvements in several difficult pattern recognition problems. In this article, we introduce and investigate robust combiners, a family of classifiers based on order statistics. We focus our study to the analysis of the decision boundaries, and how these boundaries are affected by order statistics combiners. In particular, we show that using the ith order statistic, or a linear combination of the ordered classifier outputs is quite beneficial in the presence of outliers or uneven classifier performance. Experimental results on several public domain data sets corroborate these findings.

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BibTeX Entry

@inproceedings{tumer-ghosh_ijcnn98,
        author={K. Tumer and J. Ghosh},
        title="Classifier Combining through Trimmed Means and Order
		Statistics",
        booktitle="Proceedings of the International Joint Conference 
		on Neural Networks",
        pages ="757-762",
        address="Anchorage, AL",
	month = {June},
	abstract={Combining the outputs of multiple neural networks has led to substantial improvements in several difficult pattern recognition problems. In this article, we introduce and investigate robust combiners, a family of classifiers based on order statistics. We focus our study to the analysis of the decision boundaries, and how these boundaries are affected by order statistics combiners. In particular, we show that using the ith order statistic, or a linear combination of the ordered classifier outputs is quite beneficial in the presence of outliers or uneven classifier performance. Experimental results on several public domain data sets corroborate these findings.},
	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Classifier Ensembles},
        year={1998}
}

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