  In this paper, we propose an online learning algorithm PRIL for learning
ranking classifiers using interval labeled data and show its correctness. We
show its convergence in finite number of steps if there exists an ideal
classifier such that the rank given by it for an example always lies in its
label interval. We then generalize this mistake bound result for the general
case. We also provide regret bound for the proposed algorithm. We propose a
multiplicative update algorithm for PRIL called M-PRIL. We provide its
correctness and convergence results. We show the effectiveness of PRIL by
showing its performance on various datasets.
