Semi-supervised multitask learning
Publication
, 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
ISBN
9781605603520
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
ISBN
9781605603520
Publication Date
January 1, 2008