Ranking with Large Margin Principle: Two Approaches

@inproceedings{Shashua2002RankingWL,
  title={Ranking with Large Margin Principle: Two Approaches},
  author={Amnon Shashua and Anat Levin},
  booktitle={NIPS},
  year={2002}
}
We discuss the problem of ranking 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 different margins where the sum of margins is maximized. This approach is shown to reduce to -SVM when the number of classes . Both approaches are optimal in… CONTINUE READING
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Large margin rank boundaries for ordinal regression

  • R. Herbrich, T. Graepel, K. Obermayer
  • Advances in Large Margin Classifiers,
  • 2000
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