For supervised classification tasks that involve a large number of instances, we propose and
study a new efficient tool, namely the Local-to-Global Support Vector Machine (LGSVM) …

Thanks to their easy implementation via radial basis functions (RBFs), meshfree kernel
methods have proved to be an effective tool for, eg, scattered data interpolation, PDE …

For supervised regression tasks we propose and study a new tool, namely Kriging Estimator
based on the Partition of Unity (KEPU) method. Its background belongs to the framework of …

We investigate an interpolation/extrapolation method that, given scattered observations of
the Fourier transform, approximates its inverse. The interpolation algorithm takes advantage …

For semi-supervised learning on graphs, we study how initial kernels in a supervised
learning regime can be augmented with additional information from known priors or from …

The main purpose of this work is to provide an efficient scheme for constructing kernel-
based reduced interpolation models. In the existing literature such problems are mainly …

The data driven extrapolation requires the definition of a functional model depending on the
available data and has the application scope of providing reliable predictions on the …

In recent years, various kernels have been proposed in the context of persistent homology to
deal with persistence diagrams in supervised learning approaches. In this paper, we …

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 …

Abstract Machine learning and in particular deep learning techniques are nowadays state of
the art and used in everyday life. Beyond, so-called kernel methods are another class of …