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» An Auxiliary Variational Method
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CSDA
2007
134views more  CSDA 2007»
15 years 1 months ago
Variational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
Clare A. McGrory, D. M. Titterington
DSP
2007
15 years 1 months ago
Blind separation of nonlinear mixtures by variational Bayesian learning
Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor a...
Antti Honkela, Harri Valpola, Alexander Ilin, Juha...
138
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VLSM
2005
Springer
15 years 7 months ago
A Gradient Descent Procedure for Variational Dynamic Surface Problems with Constraints
Abstract. Many problems in image analysis and computer vision involving boundaries and regions can be cast in a variational formulation. This means that m-surfaces, e.g. curves and...
Jan Erik Solem, Niels Chr. Overgaard
MOC
1998
136views more  MOC 1998»
15 years 1 months ago
Total variation diminishing Runge-Kutta schemes
In this paper we further explore a class of high order TVD (total variation diminishing) Runge-Kutta time discretization initialized in a paper by Shu and Osher, suitable for solvi...
Sigal Gottlieb, Chi-Wang Shu
123
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ECCV
2008
Springer
16 years 3 months ago
Local Regularization for Multiclass Classification Facing Significant Intraclass Variations
We propose a new local learning scheme that is based on the principle of decisiveness: the learned classifier is expected to exhibit large variability in the direction of the test ...
Lior Wolf, Yoni Donner