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A Selective Sampling Strategy for Label Ranking
[chapter]
2006
Lecture Notes in Computer Science
We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen's generalization bounds using unlabeled data [7] , initially developed in the context of classification. The bounds we obtain suggest a selective sampling strategy provided that a
doi:10.1007/11871842_7
fatcat:y4eiq5yuxrbd5i7xomxgcl5tmm