Papers Published

1. 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 (September, 2009), pp. 1637-1640, IEEE [doi] .
   (last updated on 2024/12/31)

   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. ©2009 IEEE.