Abstract

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM) based supervised DA technique, where the similarity between source and target domains is mod-eled as the similarity between their SVM decision boundaries. We couple the source and target SVMs and reduce the model to a standard single SVM. We test the Coupled-SVM on multiple datasets and compare our results with other popular SVM based DA approaches.

Original languageEnglish (US)
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1295-1298
Number of pages4
ISBN (Electronic)9781450334594
DOIs
StatePublished - Oct 13 2015
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: Oct 26 2015Oct 30 2015

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other23rd ACM International Conference on Multimedia, MM 2015
CountryAustralia
CityBrisbane
Period10/26/1510/30/15

Keywords

  • Coupled SVM
  • Supervised Domain Adaptation

ASJC Scopus subject areas

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Demakethepalli Venkateswara, H., Lade, P., Ye, J., & Panchanathan, S. (2015). Coupled support vector machines for supervised domain adaptation. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference (pp. 1295-1298). (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2733373.2806334