Coupled support vector machines for supervised domain adaptation

Hemanth Demakethepalli Venkateswara; Prasanth Lade; Jieping Ye; Sethuraman Panchanathan

doi:10.1145/2733373.2806334

Coupled support vector machines for supervised domain adaptation

Hemanth Demakethepalli Venkateswara, Prasanth Lade, Jieping Ye, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

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 language	English (US)
Title of host publication	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
Publisher	Association for Computing Machinery, Inc
Pages	1295-1298
Number of pages	4
ISBN (Electronic)	9781450334594
DOIs	https://doi.org/10.1145/2733373.2806334
State	Published - Oct 13 2015
Event	23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia Duration: Oct 26 2015 → Oct 30 2015

Publication series

Name	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other	23rd ACM International Conference on Multimedia, MM 2015
Country/Territory	Australia
City	Brisbane
Period	10/26/15 → 10/30/15

Keywords

Coupled SVM
Supervised Domain Adaptation

ASJC Scopus subject areas

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

Access to Document

10.1145/2733373.2806334

Cite this

Coupled support vector machines for supervised domain adaptation. / Demakethepalli Venkateswara, Hemanth; Lade, Prasanth; Ye, Jieping et al.
MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, 2015. p. 1295-1298 (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, Association for Computing Machinery, Inc, pp. 1295-1298, 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, Australia, 10/26/15. https://doi.org/10.1145/2733373.2806334

@inproceedings{348cd93380a9416288c03768a8b6c8b6,

title = "Coupled support vector machines for supervised domain adaptation",

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.",

keywords = "Coupled SVM, Supervised Domain Adaptation",

author = "{Demakethepalli Venkateswara}, Hemanth and Prasanth Lade and Jieping Ye and Sethuraman Panchanathan",

note = "Funding Information: National Science Foundation (NSF) Publisher Copyright: {\textcopyright} 2015 ACM.; 23rd ACM International Conference on Multimedia, MM 2015 ; Conference date: 26-10-2015 Through 30-10-2015",

year = "2015",

month = oct,

day = "13",

doi = "10.1145/2733373.2806334",

language = "English (US)",

series = "MM 2015 - Proceedings of the 2015 ACM Multimedia Conference",

publisher = "Association for Computing Machinery, Inc",

pages = "1295--1298",

booktitle = "MM 2015 - Proceedings of the 2015 ACM Multimedia Conference",

}

TY - GEN

T1 - Coupled support vector machines for supervised domain adaptation

AU - Demakethepalli Venkateswara, Hemanth

AU - Lade, Prasanth

AU - Ye, Jieping

AU - Panchanathan, Sethuraman

PY - 2015/10/13

Y1 - 2015/10/13

N2 - 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.

AB - 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.

KW - Coupled SVM

KW - Supervised Domain Adaptation

UR - http://www.scopus.com/inward/record.url?scp=84962784482&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962784482&partnerID=8YFLogxK

U2 - 10.1145/2733373.2806334

DO - 10.1145/2733373.2806334

M3 - Conference contribution

AN - SCOPUS:84962784482

T3 - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

SP - 1295

EP - 1298

BT - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

PB - Association for Computing Machinery, Inc

T2 - 23rd ACM International Conference on Multimedia, MM 2015

Y2 - 26 October 2015 through 30 October 2015

ER -

Coupled support vector machines for supervised domain adaptation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this