Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18321
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dc.contributorDepartment of Applied Social Sciencesen_US
dc.creatorDeng, Zen_US
dc.creatorChoi, KSen_US
dc.creatorJiang, Yen_US
dc.creatorWang, Sen_US
dc.date.accessioned2015-07-14T01:32:58Z-
dc.date.available2015-07-14T01:32:58Z-
dc.identifier.issn2168-2267en_US
dc.identifier.urihttp://hdl.handle.net/10397/18321-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Z. Deng, K. Choi, Y. Jiang and S. Wang, "Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods," in IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2585-2599, Dec. 2014 is available at https://dx.doi.org/10.1109/TCYB.2014.2311014.en_US
dc.subjectClassificationen_US
dc.subjectFuzzy systemsen_US
dc.subjectGeneralized hidden-mapping ridge regression (GHRR)en_US
dc.subjectInductive transfer learningen_US
dc.subjectKernel methodsen_US
dc.subjectKnowledge-leverageen_US
dc.subjectNeural networksen_US
dc.subjectRegressionen_US
dc.titleGeneralized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methodsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2585en_US
dc.identifier.epage2599en_US
dc.identifier.volume44en_US
dc.identifier.issue12en_US
dc.identifier.doi10.1109/TCYB.2014.2311014en_US
dcterms.abstractInductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted models. In this regard, the generalized hidden-mapping ridge regression (GHRR) method is introduced in order to train various types of classical intelligence models, including neural networks, fuzzy logical systems and kernel methods. Furthermore, the knowledge-leverage based transfer learning mechanism is integrated with GHRR to realize the inductive transfer learning method called transfer GHRR (TGHRR). Since the information from the induced knowledge is much clearer and more concise than that from the data in the source domain, it is more convenient to control and balance the similarity and difference of data distributions between the source and target domains. The proposed GHRR and TGHRR algorithms have been evaluated experimentally by performing regression and classification on synthetic and real world datasets. The results demonstrate that the performance of TGHRR is competitive with or even superior to existing state-of-the-art inductive transfer learning algorithms.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on cybernetics, Dec, 2014, v. 44, no. 12, p. 2585-2599en_US
dcterms.isPartOfIEEE transactions on cyberneticsen_US
dcterms.issued2014-12-
dc.identifier.scopus2-s2.0-84911938464-
dc.identifier.eissn2168-2275en_US
dc.identifier.rosgroupidr70874-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n13-
dc.identifier.SubFormID452-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU5134/12Een_US
dc.description.pubStatusPublisheden_US
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