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» Nonlinear Predictive Control with a Gaussian Process Model
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IPPS
1998
IEEE
15 years 6 months ago
Predicting the Running Times of Parallel Programs by Simulation
Predicting the running time of a parallel program is useful for determining the optimal values for the parameters of the implementation and the optimal mapping of data on processo...
Radu Rugina, Klaus E. Schauser
104
Voted
AVSS
2006
IEEE
15 years 7 months ago
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
We propose a method for create a background model in non-stationary scenes. Each pixel has a dynamic Gaussian mixture model. Our approach can automatically change the number of Ga...
Atsushi Shimada, Daisaku Arita, Rin-ichiro Taniguc...
187
Voted
ECCV
2002
Springer
16 years 2 months ago
Using Robust Estimation Algorithms for Tracking Explicit Curves
The context of this work is lateral vehicle control using a camera as a sensor. A natural tool for controlling a vehicle is recursive filtering. The well-known Kalman fil...
Jean-Philippe Tarel, Sio-Song Ieng, Pierre Charbon...
ICML
2010
IEEE
15 years 2 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
101
Voted
NIPS
2007
15 years 3 months ago
Robust Regression with Twinned Gaussian Processes
We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over laten...
Andrew Naish-Guzman, Sean B. Holden