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The RBF parameters are typically optimized using a regularization algorithm.
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We achieve this by regularizing the L2-norm of the model parameters using a regularization factor.
We present a regularization algorithm that uses a combination of analytical and numerical tools to distinguish between these two contributions and ultimately subtract the singularity.
A regularization algorithm was used to resolve for up to five multimodal populations and their polydispersity, normalized to the mean size of the peak and the hydrodynamic radius (RH).
A regularization algorithm with a computational error for treating accretive operators is investigated.
Variational inequality, fixed point and generalized equilibrium problems are investigated via a regularization algorithm.
The general form has the advantage that we can solve the various regularization problems in a unified framework using a single algorithm.
An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm.
This paper describes an improved approach, which uses the ℓ0−norm regularization algorithm with the minimum entropy based deconvolution, which gives the benefits of faster convergence of algorithms and increase robustness to additive noise and inverse filter length.
The design of the ANN was automated using a genetic algorithm (GA) with indirect binary encoding and an objective function that uses the effective number of parameters provided by Bayesian regularization.
using a weak-coupling algorithm [23].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com