short-paper

Coupled Support Vector Machines for Supervised Domain Adaptation

Authors:

Hemanth Venkateswara,

Sethuraman PanchanathanAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1295 - 1298

https://doi.org/10.1145/2733373.2806334

Published: 13 October 2015 Publication History

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 modeled 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.

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Cited By

Yang XDeng CLiu TTao D(2022)Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.302607944:4(1992-2003)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TPAMI.2020.3026079
Jin C(2021)Cross-project software defect prediction based on domain adaptation learning and optimizationExpert Systems with Applications10.1016/j.eswa.2021.114637171(114637)Online publication date: Jun-2021
https://doi.org/10.1016/j.eswa.2021.114637
Venkateswara HPanchanathan S(2020)Introduction to Domain AdaptationDomain Adaptation in Computer Vision with Deep Learning10.1007/978-3-030-45529-3_1(3-21)Online publication date: 19-Aug-2020
https://doi.org/10.1007/978-3-030-45529-3_1
Show More Cited By

Index Terms

Coupled Support Vector Machines for Supervised Domain Adaptation
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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National Science Foundation

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MM '15

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SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

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MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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The 32nd ACM International Conference on Multimedia

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Cited By

Yang XDeng CLiu TTao D(2022)Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.302607944:4(1992-2003)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TPAMI.2020.3026079
Jin C(2021)Cross-project software defect prediction based on domain adaptation learning and optimizationExpert Systems with Applications10.1016/j.eswa.2021.114637171(114637)Online publication date: Jun-2021
https://doi.org/10.1016/j.eswa.2021.114637
Venkateswara HPanchanathan S(2020)Introduction to Domain AdaptationDomain Adaptation in Computer Vision with Deep Learning10.1007/978-3-030-45529-3_1(3-21)Online publication date: 19-Aug-2020
https://doi.org/10.1007/978-3-030-45529-3_1
Neloy AHaque HUl Islam M(2019)Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features FactoringProceedings of the 2019 11th International Conference on Machine Learning and Computing10.1145/3318299.3318377(350-356)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3318299.3318377
Ding ZFu Y(2019)Deep Transfer Low-Rank Coding for Cross-Domain LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.287456730:6(1768-1779)Online publication date: Jun-2019
https://doi.org/10.1109/TNNLS.2018.2874567
Singh AChakraborty S(2019)Deep Domain Adaptation for RegressionDevelopment and Analysis of Deep Learning Architectures10.1007/978-3-030-31764-5_4(91-115)Online publication date: 2-Nov-2019
https://doi.org/10.1007/978-3-030-31764-5_4
Xie XSun SChen HQian J(2018)Domain Adaptation with Twin Support Vector MachinesNeural Processing Letters10.5555/3288065.328813348:2(1213-1226)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288065.3288133
Venkateswara HChakraborty SPanchanathan S(2017)Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature RepresentationsIEEE Signal Processing Magazine10.1109/MSP.2017.274046034:6(117-129)Online publication date: Nov-2017
https://doi.org/10.1109/MSP.2017.2740460
Xie XSun SChen HQian J(2017)Domain Adaptation with Twin Support Vector MachinesNeural Processing Letters10.1007/s11063-017-9775-348:2(1213-1226)Online publication date: 19-Dec-2017
https://doi.org/10.1007/s11063-017-9775-3
Venkateswara HChakraborty SPanchanathan S(2016)Nonlinear Embedding Transform for Unsupervised Domain AdaptationComputer Vision – ECCV 2016 Workshops10.1007/978-3-319-49409-8_36(451-457)Online publication date: 24-Nov-2016
https://doi.org/10.1007/978-3-319-49409-8_36

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