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» Robustness of the Learning with Errors Assumption
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CVPR
2010
IEEE
14 years 10 months ago
Cost-Sensitive Subspace Learning for Face Recognition
Conventional subspace learning-based face recognition aims to attain low recognition errors and assumes same loss from all misclassifications. In many real-world face recognition...
Jiwen Lu, Tan Yap-Peng
ICCV
2001
IEEE
16 years 1 months ago
Robust Principal Component Analysis for Computer Vision
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares...
Fernando De la Torre, Michael J. Black
CVPR
2007
IEEE
16 years 1 months ago
Filtered Component Analysis to Increase Robustness to Local Minima in Appearance Models
Appearance Models (AM) are commonly used to model appearance and shape variation of objects in images. In particular, they have proven useful to detection, tracking, and synthesis...
Fernando De la Torre, Alvaro Collet, Manuel Quero,...
SIAMCO
2000
75views more  SIAMCO 2000»
14 years 11 months ago
Feedback Stabilization and Lyapunov Functions
Given a locally defined, nondifferentiable but Lipschitz Lyapunov function, we construct a (discontinuous) feedback law which stabilizes the underlying system to any given toleran...
F. H. Clarke, Yu. S. Ledyaev, L. Rifford, R. J. St...
ECCV
2008
Springer
16 years 1 months ago
Semi-supervised On-Line Boosting for Robust Tracking
Abstract. Recently, on-line adaptation of binary classifiers for tracking have been investigated. On-line learning allows for simple classifiers since only the current view of the ...
Helmut Grabner, Christian Leistner, Horst Bischof