5 papers · 1 filter
Mapping for Approximation: A Unified View of Rescaled, Variably Scaled and Rational Kernel Methods
Stefano De Marchi
Classical approximation methods are usually improved by changing the sampling set, increasing the number of data points, or selecting a different basis. We advocate a complementary…
Results, challenges and new steps on RBF approximation and computation
Stefano De Marchi, Nadaniela Egidi, Josephin Giacomini +1
We present an up-to-date overview of approximation methods based on Radial Basis Function (RBF) techniques, which have recently attracted attention across various computational too…
Geometric properties of the Lebesgue function
Leokadia Białas-Cież, Stefano De Marchi, Mateusz Suder
We present a collection of observations concerning the peculiar behavior of the Lebesgue function in the setting of the interval and the square $[-1,1]^2…
-convergence of Kantorovich-type Max-Min Neural Network Operators
İsmail Aslan, Stefano De Marchi, Wolfgang Erb
In this work, we study the Kantorovich variant of max-min neural network operators, in which the operator kernel is defined in terms of sigmoidal functions. Our main aim is to demo…
Mapped Variably Scaled Kernels: Applications to Solar Imaging
Francesco Marchetti, Emma Perracchione, Anna Volpara +3
Variably scaled kernels and mapped bases constructed via the so-called fake nodes approach are two different strategies to provide adaptive bases for function interpolation. In thi…