Ranking with Large Margin Principle: Two Approaches

Shashua, Amnon, Levin, Anat

Neural Information Processing Systems 

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 ofSVM to ranking learning.

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