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Abstract: We discuss the problem of ranking k instances with the use of a "large margin" principle. We introduce two main approaches: the first is the "fixed margin" policy in which the margin of the closest neighboring classes is being maximized --- which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for k \Gamma 1 different margins where the sum of margins is maximized. This approach is shown to reduce to -SVM when the number of classes k = 2. ... (Update)
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BibTeX entry: (Update)
Amnon Shashua and Anat Levin. Ranking with large margin principle: Two approaches. In NIPS*14, 2003. http://citeseer.ist.psu.edu/article/shashua03ranking.html More
@misc{ shashua03ranking,
author = "A. Shashua and A. Levin",
title = "Ranking with large margin principle: Two approaches",
text = "Amnon Shashua and Anat Levin. Ranking with large margin principle: Two
approaches. In NIPS*14, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/article/shashua03ranking.html" }
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49 New support vector algorithms - Scholkopf, Smola et al. - 2000
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16 Support vector machines for multi-class pattern recognition - Weston, Watkins - 1999
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4 A training algorithm for optimal margin classifers (context) - Boser, Guyon et al. - 1992
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