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» Randomness, Stochasticity and Approximations
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ICML
2006
IEEE
14 years 10 months ago
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...
David Wingate, Satinder P. Singh
ISCI
2008
137views more  ISCI 2008»
13 years 9 months ago
Stochastic dominance-based rough set model for ordinal classification
In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, Dominance-based Rough Set Approach (DRSA) has...
Wojciech Kotlowski, Krzysztof Dembczynski, Salvato...
SIAMNUM
2010
96views more  SIAMNUM 2010»
13 years 3 months ago
A Stochastic Algorithm for Parametric Sensitivity in Smoluchowski's Coagulation Equation
Abstract. In this article a stochastic particle system approximation to the parametric sensitivity in the Smoluchowski coagulation equation is introduced. The parametric sensitivit...
Ismaël F. Bailleul, Peter L. W. Man, Markus K...
ECCV
2008
Springer
14 years 11 months ago
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...
Jerod J. Weinman, Lam Tran, Christopher J. Pal
AAAI
2006
13 years 10 months ago
Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems
The Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parame...
David Wingate, Satinder P. Singh