Ranking with Large Margin Principle: Two Approaches

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Amnon Shashua, Anat Levin


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 generaliza(cid:173) tion 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 lI-SVM when the number of classes k = 2. Both approaches are optimal in size of 21 where I is the total number of training examples. Experiments performed on visual classification and "collab(cid:173) orative filtering" show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.