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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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no |
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BCNPCL @ bcnpcl @ VBR2006 |
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723 |
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Fadi Dornaika; Bogdan Raducanu |
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3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2008 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ DoR2008d |
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1109 |
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Author |
Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes |
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2006 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ PRV2006a |
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646 |
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Author |
M. Bressan; Jordi Vitria |
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Title |
Independent Feature Selection |
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2003 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823) |
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OR;MV |
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BCNPCL @ bcnpcl @ BrV2003a |
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366 |
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Author |
Oriol Pujol; David Masip |
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Title |
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary |
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Journal Article |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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6 |
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1140–1146 |
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This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention |
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OR;HuPBA;MV;MILAB |
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BCNPCL @ bcnpcl @ PuM2009 |
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1252 |
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