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Author (up) Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Cost-sensitive Structured SVM for Multi-category Domain Adaptation Type Conference Article
  Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3886 - 3891  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract 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.  
  Address Stockholm; Sweden; August 2014  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN Medium  
  Area Expedition Conference ICPR  
  Notes ADAS; 600.057; 600.054; 601.217; 600.076 Approved no  
  Call Number ADAS @ adas @ XRV2014a Serial 2434  
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