A Selective Sampling Strategy for Label Ranking

Abstract. 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 K

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Massih-Reza Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari
• Keywords: Active Learning, Active Learning Strategy, ECML 2006, Label Ranking Functions, Machine Learning