Add to ORKG1.Introduction 2.Mathematical background 3.RKHS and Bayesian estimates 4.RKHS for superresolution 5.Concluding remarkWe present a few computationally tractable models of finite-dimensional Reproducing Kernel Hilbert Spaces (RKHS) that give a theoretical foundation of the techniques we have developed to solve several problems in computer graphics. The problems we deal with in this paper are signal and geometry interpolation/extrapolation as well as solving an inverse problem in animation
This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces ...
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that ...
We solve Gleason's problem in the reproducing kernel Hilbert space with reproducing kernel 1/(1 - Si...
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse ...
AbstractReproducing Kernel Hilbert Spaces (RKHSs) are a very useful and powerful tool of functional ...
Abstract. We give theoretical analysis for several learning problems on the hypercube, using the reg...
The finite energy Fourier-, Hankel-, sine-, and cosine-transformed bandlimited signals are specific ...
The function estimation in RKHS (Reproducing Kernel Hilbert Space) from finite noisy samples is a ty...
A reproducing-kernel Hilbert space approach to image inter-polation is introduced. In particular, th...
Reproducing Kernel Hilbert Spaces (RKHS) and their kernel are important tools which have been found ...
This work deals with a method for building Reproducing Kernel Hilbert Space (RKHS) from a Hilbert sp...
We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization,...
<p>Reparameterization of the Bayesian RKHS (reproducing kernel Hilbert spaces) and the G-BLUP. Adapt...
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces ...
We present an algorithm for performing attributed graph matching. This algorithm is derived from a g...
This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces ...
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that ...
We solve Gleason's problem in the reproducing kernel Hilbert space with reproducing kernel 1/(1 - Si...
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse ...
AbstractReproducing Kernel Hilbert Spaces (RKHSs) are a very useful and powerful tool of functional ...
Abstract. We give theoretical analysis for several learning problems on the hypercube, using the reg...
The finite energy Fourier-, Hankel-, sine-, and cosine-transformed bandlimited signals are specific ...
The function estimation in RKHS (Reproducing Kernel Hilbert Space) from finite noisy samples is a ty...
A reproducing-kernel Hilbert space approach to image inter-polation is introduced. In particular, th...
Reproducing Kernel Hilbert Spaces (RKHS) and their kernel are important tools which have been found ...
This work deals with a method for building Reproducing Kernel Hilbert Space (RKHS) from a Hilbert sp...
We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization,...
<p>Reparameterization of the Bayesian RKHS (reproducing kernel Hilbert spaces) and the G-BLUP. Adapt...
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces ...
We present an algorithm for performing attributed graph matching. This algorithm is derived from a g...
This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces ...
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that ...
We solve Gleason's problem in the reproducing kernel Hilbert space with reproducing kernel 1/(1 - Si...