Reproducing property of bounded linear operators and kernel regularized least square regressions

DOI10.1142/S0219691324500139MaRDI QIDQ6591721

Publication date: 22 August 2024

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

zbMATH Keywords

convergence rate bounded linear operator functional reproducing kernel Hilbert space kernel regularized regression

Mathematics Subject Classification ID

Computational learning theory (68Q32) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Rate of convergence, degree of approximation (41A25)

Cites Work

Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Learning rates of regularized regression on the unit sphere
Approximation by multivariate Bernstein-Durrmeyer operators and learning rates of least-squares regularized regression with multivariate polynomial kernels
Bernstein-type operators and their derivatives
Learning theory viewpoint of approximation by positive linear operators
Spherical harmonics and approximations on the unit sphere. An introduction
On the de la Vallée-Poussin means on the sphere
Eigenvalues of integral operators defined by smooth positive definite kernels
Cesàro summability and Marchaud inequality
Derivative reproducing properties for kernel methods in learning theory
Learning rates for regularized classifiers using multivariate polynomial kernels
A note on application of integral operator in learning theory
Multipliers for (C,a)-bounded Fourier expansions in Banach spaces and approximation theory
Fractional derivatives and best approximations
Best approximation and \(K\)-functionals
A note on Bernstein-Durrmeyer operators in \(L_ 2(S)\)
Some equivalence theorems with \(K\)-functionals
Learning rates of multi-kernel regression by orthogonal greedy algorithm
Regularization in a functional reproducing kernel Hilbert space
Learning rates for the kernel regularized regression with a differentiable strongly convex loss
Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems
Functional reproducing kernel Hilbert spaces for non-point-evaluation functional data
Localized polynomial frames on the ball
Sharp Jackson inequalities
Approximation on Banach spaces of functions on the sphere
Approximation with polynomial kernels and SVM classifiers
Shannon sampling. II: Connections to learning theory
Learning theory estimates via integral operators and their approximations
On reproducing kernel Banach spaces: generic definitions and unified framework of constructions
On the mathematical foundations of learning
Application of integral operator for vector-valued regression learning
Convergence analysis of coefficient-based regularization under moment incremental condition
Weighted approximation via Θ-summations of Fourier-Jacobi series
Summability of Fourier orthogonal series for Jacobi weight on a ball in ℝ^{𝕕}
A graph-based active learning method for classification of remote sensing images
Approximation Theory and Harmonic Analysis on Spheres and Balls
Efficiency of the weak Rescaled Pure Greedy Algorithm
Error analysis of the kernel regularized regression based on refined convex losses and RKBSs
Comparison theorems on large-margin learning
Unified error estimate for weak biorthogonal Greedy algorithms
On the K-functional in learning theory
Coefficient-based regularization network with variance loss for error
REGULARIZED LEAST SQUARE REGRESSION WITH SPHERICAL POLYNOMIAL KERNELS
An efficient multiple kernel learning in reproducing kernel Hilbert spaces (RKHS)
A representer theorem for deep kernel learning
A Convolution Structure for Jacobi Series
Optimality of the rescaled pure greedy learning algorithms
Double graphs regularized multi-view subspace clustering
Convergence analysis for kernel-regularized online regression associated with an RRKHS
Learning performance of uncentered kernel-based principal component analysis
Analysis of regularized least squares ranking with centered reproducing kernel
On the density of translation networks defined on the unit ball

This page was built for publication: Reproducing property of bounded linear operators and kernel regularized least square regressions

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6591721)