TY - GEN
T1 - Active learning for semi-supervised multi-task learning
AU - Li, Hui
AU - Liao, Xuejun
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2009/9/23
Y1 - 2009/9/23
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/4959914/
UR - http://www.scopus.com/inward/record.url?scp=70349216509&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4959914
DO - 10.1109/ICASSP.2009.4959914
M3 - Conference contribution
SN - 9781424423545
SP - 1637
EP - 1640
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -