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dc.contributor.authorLiang, Ru-Ze
dc.contributor.authorXie, Wei
dc.contributor.authorLi, Weizhi
dc.contributor.authorWang, Hongqi
dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorTaylor, Lisa
dc.date.accessioned2017-02-28T11:54:07Z
dc.date.available2017-02-28T11:54:07Z
dc.date.issued2017-01-17
dc.identifier.citationLiang R-Z, Xie W, Li W, Wang H, Wang JJ-Y, et al. (2016) A Novel Transfer Learning Method Based on Common Space Mapping and Weighted Domain Matching. 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). Available: http://dx.doi.org/10.1109/ICTAI.2016.0053.
dc.identifier.doi10.1109/ICTAI.2016.0053
dc.identifier.urihttp://hdl.handle.net/10754/622934
dc.description.abstractIn this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7814613/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.subjectlearning (artificial intelligence)
dc.subjectpattern classification
dc.subjectBenchmark testing
dc.subjectDVD
dc.subjectElectronic mail
dc.subjectLearning systems
dc.subjectMinimization
dc.subjectOptimization
dc.subjectTraining
dc.titleA Novel Transfer Learning Method Based on Common Space Mapping and Weighted Domain Matching
dc.typeConference Paper
dc.contributor.departmentMaterial Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journal2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)
dc.eprint.versionPost-print
dc.contributor.institutionVanderbilt University, Nashville, TN 37235, United States
dc.contributor.institutionSuning Commerce R&D Center USA, Inc Palo Alto, CA 94304, United States
dc.contributor.institutionSchool of Management of Harbin University of Science and Technology, Harbin 150000, China
dc.contributor.institutionNew York University Abu Dhabi, United Arab Emirates
dc.contributor.institutionMichigan State University, East Lansing, MI 48824, United States
dc.identifier.arxividarXiv:1608.04581
kaust.personLiang, Ru-Ze
refterms.dateFOA2018-06-13T16:20:59Z
dc.date.published-online2017-01-17
dc.date.published-print2016-11


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