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Variance maximization via noise injection for active sampling in learning to rank
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
Active learning for ranking, which is to selectively label the most informative examples, has been widely studied in recent years. In this paper, we propose a general active learning for ranking strategy called Variance Maximization (VM). The algorithm relies on noise injection to perturb the original unlabeled examples and generate the rank distribution of each example. Using a DCG-like gain function to measure each ranked list sampled from the rank distribution, Variance Maximization selects
doi:10.1145/2396761.2398522
dblp:conf/cikm/CaiZ12
fatcat:pkjuojyzezfbdfiglefhnuvfwy