Target learning: a novel framework to mine significant dependencies for unlabeled data

Wang, Limin, Chen, Shenglei and Mammadov, Musa 2018, Target learning: a novel framework to mine significant dependencies for unlabeled data, in PAKDD 2018 : Proceedings of the 22nd Pacific-Asia conference on knowledge and data mining, Springer, Cham, Switzerland, pp. 106-117, doi: 10.1007/978-3-319-93034-3_9.

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Title Target learning: a novel framework to mine significant dependencies for unlabeled data
Author(s) Wang, Limin
Chen, Shenglei
Mammadov, Musa
Conference name Knowledge discovery and data mining. Pacific-Asia conference (22nd : 2018 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 2018/06/03 - 2018/06/06
Title of proceedings PAKDD 2018 : Proceedings of the 22nd Pacific-Asia conference on knowledge and data mining
Editor(s) Phung, D.
Tseng, V.
Webb, G.
Ho, B.
Ganji, M.
Rashidi, L.
Publication date 2018
Series Lecture notes in computer science v.10937
Conference series Knowledge discovery and data mining Pacific-Asia conference
Start page 106
End page 117
Total pages 12
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Bayesian network
Target learning
Unlabeled data
ISBN 9783319930336
9783319930343
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-93034-3_9
Field of Research 08 Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2018, Springer International Publishing AG, part of Springer Nature
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120382

Document type: Conference Paper
Collection: School of Information Technology
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