PRIL: Perceptron Ranking Using Interval Labeled Data
This work addresses ranking problems with interval labels, which is an incremental improvement in online learning for specific applications.
The authors tackled the problem of learning ranking classifiers from interval-labeled data by proposing an online algorithm called PRIL, which they proved converges in finite steps under ideal conditions and demonstrated its effectiveness on various datasets.
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.