2014/01/30 by David Venuto, Toby Dylan Hocking, Venuto, David +5
Computer Science · Economics, Econometrics and Finance · #FOS: Computer and information sciences #Game Theory and Voting Systems #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.1401.8008
openalex publication_date 2014/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 indicates which element of the pair is better, or if there is no significant difference. 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, a real learning to rank sushi data set, and a chess data set to show that our proposed SVMcompare algorithm outperforms SVMrank when there are equality pairs.