Support vector comparison machines
This addresses ranking and comparison tasks in machine learning, particularly when equality pairs are present, but it appears incremental as it adapts existing SVM methods to a specific scenario.
The paper tackled the problem of learning from pairwise comparisons where labels indicate which element is better or if there is no significant difference, by casting it as a margin maximization and solving it with a standard SVM, showing that SVMcompare outperforms SVMrank on datasets with equality pairs.
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.