Results 41 to 50 of about 21,675 (137)
High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space. [PDF]
Math Geosci, 2020 Yao L, Dimitrakopoulos R, Gamache M.
europepmc +1 more source
Some Lemmas on Reproducing Kernel Hilbert Spaces [PDF]
, 2002 Reproducing kernel Hilbert spaces (RKHS) provides a framework for approximation from finite data using the idea of bounded linear functionals. The approximation problem in this case can be viewed as the inverse problem of finding the optimum operator from the Euclidean space of observations to some subspace of the RKHS.
Dodd, T.J., Harrison, R.F.
openaire
Bicomplex Bergman spaces on bounded domains
, 2018 The bicomplex Bergman spaces are studied for any bounded bicomplex domain. Its Bergman kernel is computed in terms of the kernels of the complex projections of the domain.
Perez-Regalado, Cesar O. +1 more
core
Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space. [PDF]
Front Neurosci, 2018 Li K, Príncipe JC.
europepmc +1 more source
Parallel Magnetic Resonance Imaging as Approximation in a Reproducing Kernel Hilbert Space. [PDF]
Inverse Probl, 2015 Athalye V, Lustig M, Uecker M.
europepmc +1 more source
Reproducing kernels in separable Hilbert spaces [PDF]
Pacific Journal of Mathematics, 1968 openaire +3 more sources
Iterative sparse interpolation in reproducing kernel Hilbert spaces [PDF]
, 2001 The problem of interpolating data in reproducing kernel Hilbert spaces is well known to be ill-conditioned. In the presence of noise, regularisation can be applied to find a good solution. In the noise-free case, regularisation has the effect of over-smoothing the function and few data points are interpolated.
Dodd, T.J., Harrison, R.F.
openaire
Combining dissimilarities in a Hyper Reproducing Kernel Hilbert Space for complex human cancer prediction. [PDF]
J Biomed Biotechnol, 2009 Martín-Merino M +2 more
europepmc +1 more source