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Active learning for semi-supervised multi-task learning

Publication ,  Journal Article
Li, H; Liao, X; Carin, L
Published in: ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
September 23, 2009

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. ©2009 IEEE.

Duke Scholars

Published In

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

DOI

ISSN

1520-6149

Publication Date

September 23, 2009

Start / End Page

1637 / 1640
 

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Li, H., Liao, X., & Carin, L. (2009). Active learning for semi-supervised multi-task learning. ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 1637–1640. https://doi.org/10.1109/ICASSP.2009.4959914
Li, H., X. Liao, and L. Carin. “Active learning for semi-supervised multi-task learning.” ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, September 23, 2009, 1637–40. https://doi.org/10.1109/ICASSP.2009.4959914.
Li H, Liao X, Carin L. Active learning for semi-supervised multi-task learning. ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. 2009 Sep 23;1637–40.
Li, H., et al. “Active learning for semi-supervised multi-task learning.” ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Sept. 2009, pp. 1637–40. Scopus, doi:10.1109/ICASSP.2009.4959914.
Li H, Liao X, Carin L. Active learning for semi-supervised multi-task learning. ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. 2009 Sep 23;1637–1640.

Published In

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

DOI

ISSN

1520-6149

Publication Date

September 23, 2009

Start / End Page

1637 / 1640