Neural fields are an interesting option for modelling macroscopic parts of the cortex involving several populations of neurons, like cortical areas. Two classes of neural field equ...
A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization error can be decomposed into two components. On...
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decompo...
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing ker...
In this paper, we develop a spatially-variant (SV) mathematical morphology theory for gray-level signals and images in the euclidean space. The proposed theory preserves the geomet...