Active learning for semi-supervised multi-task learning

Authors:
Hui Li;Xuejun Liao;Lawrence Carin
Affiliations:
Signal Innovations Group, Inc., Durham, NC, USA;Department of ECE, Duke University, Durham, NC, USA;Signal Innovations Group, Inc., Durham, NC, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Combining active learning and semi-supervised learning to construct SVM classifier

Knowledge-Based Systems

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Abstract

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.