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Cost-sensitive Structured SVM for Multi-category Domain Adaptation
Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2014. Cost-sensitive Structured SVM for Multi-category Domain Adaptation. 22nd International Conference on Pattern Recognition. IEEE, 3886–3891.
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition.
Cost-sensitive Structured SVM for Multi-category Domain AdaptationJiaolong XuSebastian RamosDavid VazquezAntonio Lopezinfo:doi/10.1109/ICPR.2014.666openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Frefbase.cvc.uab.es%2F&genre=proceeding&title=Cost-sensitive%20Structured%20SVM%20for%20Multi-category%20Domain%20Adaptation&issn=1051-4651&date=2014&spage=3886&epage=3891&aulast=Jiaolong%20Xu&au=Sebastian%20Ramos&au=David%20Vazquez&au=Antonio%20Lopez&pub=IEEE&id=info%3Adoi%2F10.1109%2FICPR.2014.666&sid=refbase%3ACVCJiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2014. Cost-sensitive Structured SVM for Multi-category Domain Adaptation. 22nd International Conference on Pattern Recognition. IEEE, 3886-3891.2014ConferencePapertextDomain AdaptationPedestrian Detectionfile:http://refbase.cvc.uab.es/files/xrv2014.pdfIEEE1051-465122nd International Conference on Pattern Recognition201438863891