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ICML
2008
IEEE

Optimizing estimated loss reduction for active sampling in rank learning

15 years 25 days ago
Optimizing estimated loss reduction for active sampling in rank learning
Learning to rank is becoming an increasingly popular research area in machine learning. The ranking problem aims to induce an ordering or preference relations among a set of instances in the input space. However, collecting labeled data is growing into a burden in many rank applications since labeling requires eliciting the relative ordering over the set of alternatives. In this paper, we propose a novel active learning framework for SVM-based and boosting-based rank learning. Our approach suggests sampling based on maximizing the estimated loss differential over unlabeled data. Experimental results on two benchmark corpora show that the proposed model substantially reduces the labeling effort, and achieves superior performance rapidly with as much as 30% relative improvement over the margin-based sampling baseline.
Pinar Donmez, Jaime G. Carbonell
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Pinar Donmez, Jaime G. Carbonell
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