424 search results - page 5 / 85 » Boosted sampling: approximation algorithms for stochastic op... |
DATE
2007
IEEE
14 years 1 months ago
2007
IEEE
In this paper we introduce a new algorithm for model order reduction in the presence of parameter or process variation. Our analysis is performed using a graph interpretation of t...
CVPR
2010
IEEE
12 years 4 months ago
2010
IEEE
Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...
ICCV
2007
IEEE
14 years 9 months ago
2007
IEEE
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
FSTTCS
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...