Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99372
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dc.contributor.authorDuan, Lixinen
dc.contributor.authorTsang, Ivor Wai-Hungen
dc.contributor.authorXu, Dongen
dc.date.accessioned2013-09-16T07:40:58Zen
dc.date.accessioned2019-12-06T20:06:32Z-
dc.date.available2013-09-16T07:40:58Zen
dc.date.available2019-12-06T20:06:32Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationDuan, L., Tsang, I. W., & Xu, D. (2012). Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 465-479.en
dc.identifier.issn0162-8828en
dc.identifier.urihttps://hdl.handle.net/10356/99372-
dc.description.abstractCross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on pattern analysis and machine intelligenceen
dc.rights© 2012 IEEEen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleDomain transfer multiple kernel learningen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1109/TPAMI.2011.114en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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