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 $\nu SVM$ when the number of classes k=2. Both approaches are optimal in size of 2l where l 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.