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ICASSP
2009
IEEE

Active learning for semi-supervised multi-task learning

10 years 4 months ago
Active learning for semi-supervised multi-task learning
We present an algorithm for active learning (adaptive selection of training data) within the context of semi-supervised multi-task classifier design. The semi-supervised multi-task classifier exploits manifold information provided by the unlabeled data, while also leveraging relevant information across multiple data sets. The active-learning component defines which data would be most informative to classifier design if the associated labels are acquired. The framework is demonstrated through application to a real landmine detection problem.
Hui Li, Xuejun Liao, Lawrence Carin
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Hui Li, Xuejun Liao, Lawrence Carin
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