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S. Tanimoto; N. Bruining; David Rotger; Petia Radeva; J. Ligthart; R.T. van Domburg; P. W. Serryus |
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Late Stent Recoil of the Bioabsorbable Everolimus Eluting Coronary Stent and its Relationship with Stent Struts Distribution and Plaque Morphology |
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2008 |
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Journal of the American College of Cardiology, vol. 52(20):1616–1620 |
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Bridgewater, NJ 08807(USA) |
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MILAB |
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BCNPCL @ bcnpcl @ TBR2008 |
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953 |
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Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2006 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6) |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VBR2006 |
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723 |
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Author |
Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2007 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) |
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BCNPCL @ bcnpcl @ VBR2007 |
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795 |
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Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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ROC curves and video analysis optimization in intestinal capsule endoscopy |
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2006 |
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Pattern Recognition Letters |
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27 |
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8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Jordi Vitria; Petia Radeva; X. Binefa |
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EigenHistograms: using low dimensional models of color distribution for real time object recognition |
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1999 |
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Ljubliana, Slovenia, Springer-Verlag |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VRB1999a |
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29 |
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