(Enter summary)
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 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. Both... (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/shashua02ranking.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/shashua02ranking.html" }
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