Guest Editorial Special Issue on Structured Multi-Output Learning: Modeling, Algorithm, Theory, and Applications

Liu, W; Shen, X; Ong, YS; Tsang, IW; Gong, C; Pavlovic, V

Guest Editorial Special Issue on Structured Multi-Output Learning: Modeling, Algorithm, Theory, and Applications

Liu, W Shen, X Ong, YS Tsang, IW Gong, C Pavlovic, V

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2020, 31, (7), pp. 2236-2239
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Shen, X
dc.contributor.author	Ong, YS
dc.contributor.author	Tsang, IW
dc.contributor.author	Gong, C
dc.contributor.author	Pavlovic, V
dc.date.accessioned	2021-01-13T02:39:18Z
dc.date.available	2021-01-13T02:39:18Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2020, 31, (7), pp. 2236-2239
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/145376
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180100106
dc.relation	http://purl.org/au-research/grants/arc/DP200101328
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems
dc.relation.isbasedon	10.1109/TNNLS.2020.2981981
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Guest Editorial Special Issue on Structured Multi-Output Learning: Modeling, Algorithm, Theory, and Applications
dc.type	Journal Article
utslib.citation.volume	31
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-13T02:39:13Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	31
utslib.citation.issue	7

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