Active learning for semi-supervised multi-task learning

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Li, H; Liao, X; Carin, L

Published Date

  • September 23, 2009

Published In

Start / End Page

  • 1637 - 1640

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2009.4959914

Citation Source

  • Scopus