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ICML
2009
IEEE

Importance weighted active learning

14 years 4 months ago
Importance weighted active learning
We propose an importance weighting framework for actively labeling samples. This technique yields practical yet sound active learning algorithms for general loss functions. Experiments on passively labeled data show that this approach effectively reduces the label complexity required to achieve good prediction performance on many learning problems.
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Alina Beygelzimer, Sanjoy Dasgupta, John Langford
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