Transfer Learning using Computational Intelligence: A Survey

Lu, J; Behbood, V; Hao, P; Zuo, H; Xue, S; Zhang, G

Transfer Learning using Computational Intelligence: A Survey

Lu, J Behbood, V Hao, P Zuo, H Xue, S Zhang, G

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2015, 80 pp. 14 - 23
Issue Date:: 2015-05-11

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Field	Value	Language
dc.contributor.author	Lu, J
dc.contributor.author	Behbood, V
dc.contributor.author	Hao, P
dc.contributor.author	Zuo, H
dc.contributor.author	Xue, S
dc.contributor.author	Zhang, G
dc.date.accessioned	2015-10-06T05:33:28Z
dc.date.available	2020-05-25T19:03:43Z
dc.date.issued	2015-05-11
dc.identifier.citation	Knowledge-Based Systems, 2015, 80 pp. 14 - 23
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/37414
dc.description.abstract	Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ...
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isreplacedby	10453/33985
dc.relation.isreplacedby	http://hdl.handle.net/10453/33985
dc.rights	(c) 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/	en_AU
dc.title	Transfer Learning using Computational Intelligence: A Survey
dc.type	Journal article
utslib.citation.volume	80
pubs.embargo.period	Not known
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 - Quantum Computation and Intelligent Systems
utslib.copyright.status	open_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.consider-herdc	false
pubs.merge-to	10453/33985
pubs.merge-to	http://hdl.handle.net/10453/33985
pubs.deleted	1970-01-01T00:00:00.0+1000

Abstract:

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ...

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http://hdl.handle.net/10453/37414