Semi-supervised multitask learning

Qiuhua Liu; Xuejun Liao; Lawrence Carin

Semi-supervised multitask learning

Qiuhua Liu, Xuejun Liao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
State	Published - Dec 1 2009
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

@inproceedings{441874185e0640559ba794a4e53b4013,

title = "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 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 = "Qiuhua Liu and Xuejun Liao and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2009",

month = dec,

day = "1",

language = "English (US)",

isbn = "160560352X",

booktitle = "Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference",

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T1 - Semi-supervised multitask learning

AU - Liu, Qiuhua

AU - Liao, Xuejun

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84863376204&partnerID=8YFLogxK

M3 - Conference contribution

SN - 160560352X

BT - Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

ER -

Semi-supervised multitask learning

Abstract

Bibliographical note

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