Optimal subset selection for active learning

Fu, Y; Zhu, X

Optimal subset selection for active learning

Fu, Y Zhu, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2011, 2 pp. 1776 - 1777
Issue Date:: 2011-11-02

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Field	Value	Language
dc.contributor.author	Fu, Y	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2011-11-02	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2011, 2 pp. 1776 - 1777	en_US
dc.identifier.isbn	9781577355090	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30818
dc.description.abstract	Active learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from two dimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem to select an optimal subset with maximum utility value. Experiments demonstrate the algorithm performance in comparison with baseline approaches. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Optimal subset selection for active learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Francisco, California, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Active learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from two dimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem to select an optimal subset with maximum utility value. Experiments demonstrate the algorithm performance in comparison with baseline approaches. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/30818