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 language||English (US)|
|Title of host publication||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Number of pages||4|
|State||Published - Sep 23 2009|