Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140512/1/6.2015-1956.pd
In this paper, we present a prediction-based dynamic programming control approach, a nonlinear model...
Abstract. Optimal prediction is a computational method for systems that cannot be properly resolved,...
We consider maximum a posteriori parameter estima-tion for structured output prediction with exponen...
SIGLEAvailable from British Library Document Supply Centre- DSC:D38230/81 / BLDSC - British Library ...
Galerkin and Petrov–Galerkin methods are some of the most successful solution procedures in numerica...
The paper is concerned with system reduction by statistical methods and, in par-ticular, by the opti...
Most approaches to structured output prediction rely on a hypothesis space of prediction functions t...
A two-step hybrid perturbation-Galerkin method is investigated using computer algebra. The technique...
SIGLEAvailable from British Library Document Supply Centre-DSC:3597.760(no 01/512) / BLDSC - British...
The Wiener–Kolmogorov principle of minimizing the mean square estimation error is discussed in the f...
Optimal prediction is a general system reduction technique for large sets of differential equations....
this paper, we propose combining three prediction mechanisms into a hybrid predictor. Each predictor...
Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of com...
An efficient and reliable method for the prediction of outputs of interest of partial differential e...
We study the predict+optimise problem, where machine learning and combinatorial optimisation must in...
In this paper, we present a prediction-based dynamic programming control approach, a nonlinear model...
Abstract. Optimal prediction is a computational method for systems that cannot be properly resolved,...
We consider maximum a posteriori parameter estima-tion for structured output prediction with exponen...
SIGLEAvailable from British Library Document Supply Centre- DSC:D38230/81 / BLDSC - British Library ...
Galerkin and Petrov–Galerkin methods are some of the most successful solution procedures in numerica...
The paper is concerned with system reduction by statistical methods and, in par-ticular, by the opti...
Most approaches to structured output prediction rely on a hypothesis space of prediction functions t...
A two-step hybrid perturbation-Galerkin method is investigated using computer algebra. The technique...
SIGLEAvailable from British Library Document Supply Centre-DSC:3597.760(no 01/512) / BLDSC - British...
The Wiener–Kolmogorov principle of minimizing the mean square estimation error is discussed in the f...
Optimal prediction is a general system reduction technique for large sets of differential equations....
this paper, we propose combining three prediction mechanisms into a hybrid predictor. Each predictor...
Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of com...
An efficient and reliable method for the prediction of outputs of interest of partial differential e...
We study the predict+optimise problem, where machine learning and combinatorial optimisation must in...
In this paper, we present a prediction-based dynamic programming control approach, a nonlinear model...
Abstract. Optimal prediction is a computational method for systems that cannot be properly resolved,...
We consider maximum a posteriori parameter estima-tion for structured output prediction with exponen...
DOI
Add to ORKG