2007
NIPS
NeurIPS 2007
Semi-Supervised Multitask Learning
Abstract
A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M par- tially 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 im- provements in generalization performance over either semi-supervised single-task learning (STL) or supervised MTL.
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Keyword Pioneer
— multitask learning
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Hot Topic Early Bird
— semi-supervised learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Trend Setter
— Multi-Task Learning
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Paradigms > Multi-Task Learning
Machine Learning > Learning Paradigms > Semi-Supervised Learning