Active learning for semi-supervised multi-task learning

Hui Li, Xuejun Liao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Number of pages4
StatePublished - Sep 23 2009
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09


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