VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY-Reference-Cited by-同舟云学术

VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY

Published:2010-01 Issue:01 Volume:08 Page:19-61
ISSN:0219-5305
Container-title:Analysis and Applications
language:en
Short-container-title:Anal. Appl.

Author:

CARMELI C.¹²,DE VITO E.³²,TOIGO A.⁴²,UMANITÀ V.⁴⁵

Affiliation:

1. Dipartimento di Fisica, Università di Genova, Italy

2. I.N.F.N., Sezione di Genova, Via Dodecaneso 33, Genova, 16146, Italy

3. Dipartimento di Scienze per l'Architettura, Stradone S. Agostino 37, Genova, 16123, Italy

4. Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, Via Dodecaneso 35, Genova, 16146, Italy

5. Dipartimento di Matematica "F. Brioschi", Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy

Abstract

This paper is devoted to the study of vector valued reproducing kernel Hilbert spaces. We focus on two aspects: vector valued feature maps and universal kernels. In particular, we characterize the structure of translation invariant kernels on abelian groups and we relate it to the universality problem.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Analysis

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219530510001503

Reference28 articles.

1. Theory of reproducing kernels

2. Optimal Rates for the Regularized Least-Squares Algorithm

3. VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES OF INTEGRABLE FUNCTIONS AND MERCER THEOREM

Cited by 52 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Approximation of optimization problems with constraints through kernel Sum-of-Squares;Optimization;2024-03-11

2. Weighted signature kernels;The Annals of Applied Probability;2024-02-01

3. Dynamic Mode Decomposition of Control-Affine Nonlinear Systems Using Discrete Control Liouville Operators;IEEE Control Systems Letters;2024

4. A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G;IEEE Communications Surveys & Tutorials;2024

5. Gradient-Free Kernel Conditional Stein Discrepancy goodness of fit testing;Machine Learning with Applications;2023-06