Semi-Supervised Multitask Learning

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 softsharing 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.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Qiuhua Liu, Xuejun Liao, Lawrence Carin
• Keywords: Information Technology, NIPS 2007, Semi-supervised Classifiers, Semi-supervised Mtl Yields, Semi-supervised Multitask