Joint structure feature exploration and regularization for multi-task graph classification

Pan, S; Wu, J; Zhuy, X; Zhang, C; Yuz, PS

Joint structure feature exploration and regularization for multi-task graph classification

Pan, S

Wu, J

Zhuy, X Zhang, C

Yuz, PS

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 1474 - 1475
Issue Date:: 2016-06-22

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Field	Value	Language
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Wu, J https://orcid.org/0000-0002-1371-5801	en_US
dc.contributor.author	Zhuy, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Yuz, PS	en_US
dc.date.issued	2016-06-22	en_US
dc.identifier.citation	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 1474 - 1475	en_US
dc.identifier.isbn	9781509020195	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106349
dc.description.abstract	© 2016 IEEE. We formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative subgraphs shared by all tasks for learning. More details can be found in [1].	en_US
dc.relation.ispartof	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016	en_US
dc.relation.isbasedon	10.1109/ICDE.2016.7498381	en_US
dc.title	Joint structure feature exploration and regularization for multi-task graph classification	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. We formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative subgraphs shared by all tasks for learning. More details can be found in [1].

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

http://hdl.handle.net/10453/106349