Pages that link to "Item:Q5962345"
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The following pages link to Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory (Q5962345):
Displaying 48 items.
- Support vector machine adapted Tikhonov regularization method to solve Dirichlet problem (Q278668) (← links)
- Linear and nonlinear approximation of spherical radial basis function networks (Q290797) (← links)
- Learning rates of regularized regression on the unit sphere (Q365838) (← links)
- Covering numbers of Gaussian reproducing kernel Hilbert spaces (Q555034) (← links)
- Approximation of reachable sets using optimal control and support vector machines (Q730517) (← links)
- Approximation properties of certain operator-induced norms on Hilbert spaces (Q765689) (← links)
- Comparing fixed and variable-width Gaussian networks (Q889280) (← links)
- Derivative reproducing properties for kernel methods in learning theory (Q939547) (← links)
- Learnability in Hilbert spaces with reproducing kernels (Q1599198) (← links)
- Nonparametric regression using needlet kernels for spherical data (Q1633627) (← links)
- The regularized least squares algorithm and the problem of learning halfspaces (Q1944907) (← links)
- Worst-case optimal approximation with increasingly flat Gaussian kernels (Q1987757) (← links)
- Gaussian kernel quadrature at scaled Gauss-Hermite nodes (Q2009110) (← links)
- A least square point of view to reproducing kernel methods to solve functional equations (Q2009588) (← links)
- Interpolation and best approximation for spherical radial basis function networks (Q2015218) (← links)
- Kernel-based interpolation at approximate Fekete points (Q2021778) (← links)
- p-kernel Stein variational gradient descent for data assimilation and history matching (Q2040686) (← links)
- On Gaussian kernels on Hilbert spaces and kernels on hyperbolic spaces (Q2139169) (← links)
- Learning from non-random data in Hilbert spaces: an optimal recovery perspective (Q2143167) (← links)
- Stochastic saddle-point optimization for the Wasserstein barycenter problem (Q2162697) (← links)
- On the mathematical foundations of stable RKHSs (Q2188280) (← links)
- On a collocation point of view to reproducing kernel methods (Q2245770) (← links)
- On the positivity and magnitudes of Bayesian quadrature weights (Q2302459) (← links)
- Kolmogorov widths on the sphere via eigenvalue estimates for Hölderian integral operators (Q2422178) (← links)
- Approximating and learning by Lipschitz kernel on the sphere (Q2514958) (← links)
- Nonlinear and additive principal component analysis for functional data (Q2657189) (← links)
- Small sample spaces for Gaussian processes (Q2692515) (← links)
- Low-rank kernel approximation of Lyapunov functions using neural networks (Q2696116) (← links)
- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings (Q2954278) (← links)
- On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification (Q4559702) (← links)
- (Q4832221) (← links)
- Reproducing Properties of Differentiable Mercer-Like Kernels on the Sphere (Q4899072) (← links)
- Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces (Q4987934) (← links)
- Integration in reproducing kernel Hilbert spaces of Gaussian kernels (Q4999474) (← links)
- (Q5159455) (← links)
- Learning Rates of <i>l<sup>q</sup></i> Coefficient Regularization Learning with Gaussian Kernel (Q5175497) (← links)
- Asymptotic Distribution for Regression in A Symmetric Periodic Gaussian Kernel Hilbert Space (Q5226619) (← links)
- Learning Rates for Classification with Gaussian Kernels (Q5380881) (← links)
- Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes (Q5862898) (← links)
- Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems (Q5868548) (← links)
- An approach to the Gaussian RBF kernels via Fock spaces (Q5884798) (← links)
- Efficient kernel canonical correlation analysis using Nyström approximation (Q6149897) (← links)
- On a nonlinear extension of the principal fitted component model (Q6168914) (← links)
- Ensemble forecasts in reproducing kernel Hilbert space family (Q6191535) (← links)
- Learning with centered reproducing kernels (Q6496339) (← links)
- Orthonormal expansions for translation-invariant kernels (Q6581767) (← links)
- Moduli of smoothness, \(K\)-functionals and Jackson-type inequalities associated with Kernel function approximation in learning theory (Q6587592) (← links)
- On sufficient dimension reduction for functional data: inverse moment-based methods (Q6600368) (← links)
