Abstract

Abstract

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 size of ©� � where � is the total number of training examples. Experiments performed on visual classification and “collaborative filtering ” show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.