Add to ORKGThe theory of reproducing kernel has been recognized as a useful instrument in several areas of mathematical research. This has been verified by researches done by Aronszajn (1950), Berlinet et al. (2003), Burbea (1976), Hille (1972), Li et al. and Wahba (1998), (2003). Many statistical problems can also be solved using models related to reproducing kernel Hilbert space (RKHS)
W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób ko...
International audienceIn this paper, we introduce a new distribution regression model for probabilit...
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to...
Numerous researchers are enthusiastic about statistical modeling to estimate the survival for patien...
A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is ext...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
Abstract. We focus on covariance criteria for finding a suitable subspace for regression in a reprod...
For an open subset\Omega of j R, an integer m, and a positive real parameter ø , the Sobolev space...
So far... ◮ Introduction to RKHS ◮ RKHS based learning algorithms ◮ Kernel PCA ◮ Kernel regression ◮...
This paper considers incorporating information on disease progression in the analysis of survival. A...
<p>Reparameterization of the Bayesian RKHS (reproducing kernel Hilbert spaces) and the G-BLUP. Adapt...
In statistical analyses, especially those using a multiresponse regression model approach, a mathema...
We define F to be a reproducing kernel Hilbert space on domain X with feature map φ(x) and kernel k(...
This report is concerned with the theory of reproducing kernels. First, a background of elementary f...
Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cas...
W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób ko...
International audienceIn this paper, we introduce a new distribution regression model for probabilit...
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to...
Numerous researchers are enthusiastic about statistical modeling to estimate the survival for patien...
A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is ext...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
Abstract. We focus on covariance criteria for finding a suitable subspace for regression in a reprod...
For an open subset\Omega of j R, an integer m, and a positive real parameter ø , the Sobolev space...
So far... ◮ Introduction to RKHS ◮ RKHS based learning algorithms ◮ Kernel PCA ◮ Kernel regression ◮...
This paper considers incorporating information on disease progression in the analysis of survival. A...
<p>Reparameterization of the Bayesian RKHS (reproducing kernel Hilbert spaces) and the G-BLUP. Adapt...
In statistical analyses, especially those using a multiresponse regression model approach, a mathema...
We define F to be a reproducing kernel Hilbert space on domain X with feature map φ(x) and kernel k(...
This report is concerned with the theory of reproducing kernels. First, a background of elementary f...
Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cas...
W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób ko...
International audienceIn this paper, we introduce a new distribution regression model for probabilit...
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to...