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Reproducing kernel Hilbert spaces are uniquely characterized by their kernel, but reproducing kernel Banach spaces (RKBS) are not. However, a characterization of which RKBS admit a given kernel as reproducing kernel is lacking. This work…

Functional Analysis · Mathematics 2026-03-31 Tjeerd Jan Heeringa

We study kernel functions, and associated reproducing kernel Hilbert spaces $\mathscr{H}$ over infinite, discrete and countable sets $V$. Numerical analysis builds discrete models (e.g., finite element) for the purpose of finding…

Functional Analysis · Mathematics 2015-08-17 Palle Jorgensen , Feng Tian

The role of kernels is central to machine learning. Motivated by the importance of power-law distributions in statistical modeling, in this paper, we propose the notion of power-law kernels to investigate power-laws in learning problem. We…

Machine Learning · Computer Science 2013-04-02 Debarghya Ghoshdastidar , Ambedkar Dukkipati

We study integro-differential inclusions in Hilbert spaces with operator-valued kernels and give sufficient conditions for the well-posedness. We show that several types of integro-differential equations and inclusions are covered by the…

Analysis of PDEs · Mathematics 2015-06-17 Sascha Trostorff

We present decompositions of various positive kernels as integrals or sums of positive kernels. Within this framework we study the reproducing kernel Hilbert spaces associated with the fractional and bi-fractional Brownian motions. As a…

Probability · Mathematics 2007-05-23 Daniel Alpay , David Levanony

We study classes of reproducing kernels $K$ on general domains; these are kernels which arise commonly in machine learning models; models based on certain families of reproducing kernel Hilbert spaces. They are the positive definite kernels…

Functional Analysis · Mathematics 2017-08-22 Palle Jorgensen , Feng Tian

This paper considers paired operators in the context of the Lebesgue Hilbert space on the unit circle and its subspace, the Hardy space $H^2$. The kernels of such operators, together with their analytic projections, which are…

Functional Analysis · Mathematics 2024-02-09 M. Cristina Câmara , Jonathan R. Partington

A general theory of reproducing kernels and reproducing kernel Hilbert spaces on a right quaternionic Hilbert space is presented. Positive operator valued measures and their connection to a class of generalized quaternionic coherent states…

Mathematical Physics · Physics 2017-09-11 K. Thirulogasanthar , S. Twareque Ali

This paper focuses on the use of the theory of Reproducing Kernel Hilbert Spaces in the statistical analysis of replicated point processes. We show that spatial point processes can be observed as random variables in a Reproducing Kernel…

Methodology · Statistics 2023-01-06 Amelia Simó

In this paper we introduce reproducing kernel Hilbert spaces of polyanalytic functions of infinite order. First we study in details the counterpart of the Fock space and related results in this framework. In this case the kernel function is…

Complex Variables · Mathematics 2021-12-30 Daniel Alpay , Fabrizio Colombo , Kamal Diki , Irene Sabadini

The purpose of this work is to explore the existence and properties of reproducing kernel Hilbert subspaces of $L^2(\C, \, d^2z/\pi)$ based on subsets of complex Hermite polynomials. The resulting coherent states (CS) form a family…

Mathematical Physics · Physics 2015-06-12 S. Twareque Ali , Fabio Bagarello , Jean Pierre Gazeau

In this article, the reproducing kernel Hilbert space [0, 1] is employed for solving a class of third-order periodic boundary value problem by using fitted reproducing kernel algorithm. The reproducing kernel function is built to get fast…

Numerical Analysis · Mathematics 2017-04-18 Asad Freihat , Radwan Abu-Gdairi , Hammad Khalil , Eman Abuteen , Mohammed Al-Smadi , Rahmat Ali Khan

We suppose that $G$ is a locally compact abelian group, $Y$ is a measure space, and $H$ is a reproducing kernel Hilbert space on $G\times Y$ such that $H$ is naturally embedded into $L^2(G\times Y)$ and it is invariant under the…

Operator Algebras · Mathematics 2025-04-29 Shubham R. Bais , Egor A. Maximenko , D. Venku Naidu

Reproducing kernel Hilbert spaces (RKHSs) are very important function spaces, playing an important role in machine learning, statistics, numerical analysis and pure mathematics. Since Lipschitz and H\"older continuity are important…

Functional Analysis · Mathematics 2023-10-30 Christian Fiedler

We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant. The lower bound holds for any choice of kernel bandwidth, even if selected…

Machine Learning · Statistics 2018-12-31 Alexander Rakhlin , Xiyu Zhai

We analyse the convergence of sampling algorithms for functions in reproducing kernel Hilbert spaces (RKHS). To this end, we discuss approximation properties of kernel regression under minimalistic assumptions on both the kernel and the…

Machine Learning · Statistics 2025-04-21 Armin Iske

In this note, we describe the backward shift invariant subspaces for a large class of reproducing kernel Hilbert spaces. This class includes in particular de Branges-Rovnyak spaces (the non-extreme case) and the range space of co-analytic…

Functional Analysis · Mathematics 2019-04-08 Emmanuel Fricain , Javad Mashreghi , Rishika Rupam

We prove two new equivalences of the Feichtinger conjecture that involve reproducing kernel Hilbert spaces. We prove that if for every Hilbert space, contractively contained in the Hardy space, each Bessel sequence of normalized kernel…

Functional Analysis · Mathematics 2010-12-07 Sneh Lata , Vern I. Paulsen

We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian manifold. In particular, we discuss under which condition Sobolev spaces are RKHS and characterize their reproducing kernels. Further, we introduce and discuss a class of…

Functional Analysis · Mathematics 2019-05-28 Ernesto De Vito , Nicole Mücke , Lorenzo Rosasco

This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely…

Machine Learning · Statistics 2025-06-24 Motonobu Kanagawa , Philipp Hennig , Dino Sejdinovic , Bharath K. Sriperumbudur