Hence, for solving ERM model, we consider the following smoothing approximation problem of (2.1): min_{(x,y,z)} theta_{mu} x,y,z):= mathbb{E} bigl[ biglVert F x,y,z,xi) bigrVert ^{2}+ biglVert phi_{mu} x,y) bigrVert ^{2} bigr].
Low-rank matrix approximation problem is a field which arises in a number of applications in model selection, system identification, complexity theory, and optics.
Hence, we first discuss the corresponding analog approximation problem.
This model approximation process is summarized in the Methods.
Moreover, since the ENRBF model is initially proposed for the regression and function approximation problems, a further step is taken in this work to modify the ENRBF model to deal with the classification problems.
Step 2. Approximation problems.
Using the approximation models, the problem is reduced to a linear system of equations, which has as unknowns the nodal values of the sources intensities.
The classical Picard Lindelöf successive approximations scheme is applied to the model validation problem, namely, existence and uniqueness of solution process.
This paper derives a mixed frequency-transient model as approximation to the original problem.
The accuracy of the method is demonstrated over three model problems where the PN approximation is compared against Monte Carlo simulations and other state-of-the-art methods.
