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» Approximate algorithms for neural-Bayesian approaches
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FGR
2006
IEEE
217views Biometrics» more  FGR 2006»
15 years 4 months ago
Face Recognition with Image Sets Using Hierarchically Extracted Exemplars from Appearance Manifolds
An unsupervised nonparametric approach is proposed to automatically extract representative face samples (exemplars) from a video sequence or an image set for multipleshot face rec...
Wei Fan, Dit-Yan Yeung
IJCNN
2006
IEEE
15 years 4 months ago
Learning to Rank by Maximizing AUC with Linear Programming
— Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approxi...
Kaan Ataman, W. Nick Street, Yi Zhang
VISUALIZATION
1999
IEEE
15 years 2 months ago
Construction of Vector Field Hierarchies
We present a method for the hierarchical representation of vector fields. Our approach is based on iterative refinement using clustering and principal component analysis. The inpu...
Bjørn Heckel, Gunther H. Weber, Bernd Haman...
ALMOB
2006
80views more  ALMOB 2006»
14 years 10 months ago
Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics
The technique of Finite Markov Chain Imbedding (FMCI) is a classical approach to complex combinatorial problems related to sequences. In order to get efficient algorithms, it is k...
Grégory Nuel
NIPS
1998
14 years 11 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore