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Spotlight
Semi-Supervised Multitask Learning
Qiuhua Liu · Xuejun Liao · Lawrence Carin
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.
Author Information
Qiuhua Liu (Duke University)
Xuejun Liao (Duke University)
Lawrence Carin (KAUST)
Related Events (a corresponding poster, oral, or spotlight)
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2007 Poster: Semi-Supervised Multitask Learning »
Tue. Dec 4th 06:30 -- 06:40 PM Room
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