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PAMI
2002
114views more  PAMI 2002»
13 years 10 months ago
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
Baback Moghaddam
SADM
2010
128views more  SADM 2010»
13 years 8 months ago
Online training on a budget of support vector machines using twin prototypes
: This paper proposes twin prototype support vector machine (TVM), a constant space and sublinear time support vector machine (SVM) algorithm for online learning. TVM achieves its ...
Zhuang Wang, Slobodan Vucetic
NECO
2011
13 years 5 months ago
Least Squares Estimation Without Priors or Supervision
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
Martin Raphan, Eero P. Simoncelli
TNN
2010
234views Management» more  TNN 2010»
13 years 4 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
FGR
2011
IEEE
227views Biometrics» more  FGR 2011»
13 years 1 months ago
Warp that smile on your face: Optimal and smooth deformations for face recognition
— In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is consid...
Tobias Gass, Leonid Pishchulin, Philippe Dreuw, He...