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NIPS
2000
14 years 10 months ago
Sparse Representation for Gaussian Process Models
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a...
Lehel Csató, Manfred Opper
ICIP
2003
IEEE
15 years 11 months ago
A Bayesian framework for Gaussian mixture background modeling
Background subtraction is an essential processing component for many video applications. However, its development has largely been application driven and done in ad hoc manners. I...
Dar-Shyang Lee, Jonathan J. Hull, Berna Erol
NIPS
2003
14 years 10 months ago
Bayesian Color Constancy with Non-Gaussian Models
We present a Bayesian approach to color constancy which utilizes a nonGaussian probabilistic model of the image formation process. The parameters of this model are estimated direc...
Charles R. Rosenberg, Thomas P. Minka, Alok Ladsar...
ICASSP
2010
IEEE
14 years 8 months ago
Bayesian compressive sensing for phonetic classification
In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training...
Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, B...
ICML
2005
IEEE
15 years 10 months ago
Reinforcement learning with Gaussian processes
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Yaakov Engel, Shie Mannor, Ron Meir