Semi-supervised multitask learning
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, Conference
Liu, Q; Liao, X; Carin, L
Published in: Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference
January 1, 2008
A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M partially labeled data manifolds, are learned jointly under the constraint of a soft-sharing prior imposed over the parameters of the classifiers. The unlabeled data are utilized by basing classifier learning on neighborhoods, induced by a Markov random walk over a graph representation of each manifold. Experimental results on real data sets demonstrate that semi-supervised MTL yields significant improvements in generalization performance over either semi-supervised single-task learning (STL) or supervised MTL.
Duke Scholars
Published In
Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference
Publication Date
January 1, 2008
Citation
APA
Chicago
ICMJE
MLA
NLM
Liu, Q., Liao, X., & Carin, L. (2008). Semi-supervised multitask learning. In Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference.
Liu, Q., X. Liao, and L. Carin. “Semi-supervised multitask learning.” In Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference, 2008.
Liu Q, Liao X, Carin L. Semi-supervised multitask learning. In: Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference. 2008.
Liu, Q., et al. “Semi-supervised multitask learning.” Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference, 2008.
Liu Q, Liao X, Carin L. Semi-supervised multitask learning. Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference. 2008.
Published In
Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference
Publication Date
January 1, 2008