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Abstract: The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be optimal for the original multicategory problem. We propose multicategory SVMs, which extend the binary SVM to the multicategory case, and... (Update)
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BibTeX entry: (Update)
Y. Lee, Y. Lin, and G. Wahba.Multicategory support vector machines.Technical Report 1043, Department of Statistics, University of Wisconsin, Madison WI, 2001. http://citeseer.ist.psu.edu/lee01multicategory.html More
@inproceedings{ leelinwahba01,
author = {Y. Lee and Y. Lin and G. Wahba},
title = {Multicategory support vector machines},
booktitle = {Proceedings of the 33rd Symposium on the Interface},
text={Y. Lee, Y. Lin, and G. Wahba. Multicategory support vector machines.
Technical Report 1043, Department of Statistics, University of Wisconsin,
Madison WI, 2001. To appear, Proceedings of the 33rd Symposium on the
Interface, 2001},
year={2001},
url = {citeseer.ist.psu.edu/lee01multicategory.html} }
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23 A unified framework for regularization networks and support .. - Evgeniou, Pontil et al. - 1999
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13 Support vector machines and the Bayes rule in classification - Lin - 1999
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