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Article: Online heterogeneous transfer by hedge ensemble of offline and online decisions

TitleOnline heterogeneous transfer by hedge ensemble of offline and online decisions
Authors
KeywordsCo-occurrence data
heterogeneous transfer learning (HTL)
online learning
hedge weighting
Issue Date2018
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29, n. 7, p. 3252-3263 How to Cite?
Abstract© 2012 IEEE. In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.
Persistent Identifierhttp://hdl.handle.net/10722/276555
ISSN
2023 Impact Factor: 10.2
2023 SCImago Journal Rankings: 4.170
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYan, Yuguang-
dc.contributor.authorWu, Qingyao-
dc.contributor.authorTan, Mingkui-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorMin, Huaqing-
dc.contributor.authorTsang, Ivor W.-
dc.date.accessioned2019-09-18T08:33:58Z-
dc.date.available2019-09-18T08:33:58Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2018, v. 29, n. 7, p. 3252-3263-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/276555-
dc.description.abstract© 2012 IEEE. In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.subjectCo-occurrence data-
dc.subjectheterogeneous transfer learning (HTL)-
dc.subjectonline learning-
dc.subjecthedge weighting-
dc.titleOnline heterogeneous transfer by hedge ensemble of offline and online decisions-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2017.2751102-
dc.identifier.pmid29028211-
dc.identifier.scopuseid_2-s2.0-85031809891-
dc.identifier.volume29-
dc.identifier.issue7-
dc.identifier.spage3252-
dc.identifier.epage3263-
dc.identifier.eissn2162-2388-
dc.identifier.isiWOS:000436420400047-
dc.identifier.issnl2162-237X-

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