Asymmetric kernels are quite useful for the estimation of density functions with bounded support. Gamma kernels are designed to handle density functions whose supports are bounded from one end only, whereas beta kernels ...

Given a set B of d-dimensional boxes (i.e., axis-aligned hyperrectangles), a minimum coverage kernel is a subset of B of minimum size covering the same region as B. Computing it is NP-hard, but as for many similar NP-hard ...

We propose a fast classifier that is able to predict atrial fibrillation inducibility in patient-specific cardiac models. Our classifier is general and it does not require re-training for new anatomies, fibrosis patterns, ...

We consider the Minimum Coverage Kernel problem: given a set B of d-dimensional boxes, find a subset of B of minimum size covering the same region as B. This problem is NP -hard, but as for many NP -hard problems on graphs, ...

The modeling of real-world problems as graphs along with the problem of non-linear distributions comes up with the idea of applying kernel functions in feature spaces. Roughly speaking, the idea is to seek for well-behaved ...