PRIL: Perceptron Ranking Using Interval Labels

In this paper, we propose an online learning algorithm called PRIL for learning ranking classifiers using interval labeled data. We show the correctness of PRIL by showing that it preserves the orderings of the thresholds in successive trials. We show that the proposed algorithm converges in finite number of steps if there exists an ideal classifier. We also give the mistake bound for the general case and provide O(root T) regret bound for the proposed algorithm. We show the effectiveness of PRIL by comparing its performance with other approaches.