Résumé: In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label {-1,0,1} indicates which element of the pair is better {-1, 1}, or if there is no significant difference {0}. We cast the learning problem as a margin maximization, and show that it can be solved by converting it to a standard SVM. We use simulated nonlinear patterns and a real learning to rank sushi data set to show that our proposed SVMcompare algorithm outperforms SVMrank when there are equality y=0 labels. In addition, we show that SVMcompare outperforms the ELO rating system when predicting the outcome of chess matches.

Note: La présentation sera donnée en anglais.