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A Primer on Reproducing Kernel Hilbert Spaces [PDF]
Foundations and Trends® in Signal Processing, 2015 Reproducing kernel Hilbert spaces are elucidated without assuming prior familiarity with Hilbert spaces. Compared with extant pedagogic material,greater care is placed on motivating the definition of reproducing kernel Hilbert spaces and explaining when and why these spaces are efficacious.
Jonathan H. Manton +1 more
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Metamorphosis of images in reproducing kernel Hilbert spaces [PDF]
Advances in Computational Mathematics, 2015 Metamorphosis is a method for diffeomorphic matching of shapes, with many potential applications for anatomical shape comparison in medical imagery, a problem which is central to the field of computational anatomy. An important tool for the practical application of metamorphosis is a numerical method based on shooting from the initial momentum, as this
Casey L. Richardson, Laurent Younes
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ON THE INCLUSION RELATION OF REPRODUCING KERNEL HILBERT SPACES [PDF]
Analysis and Applications, 2013 To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established.
Haizhang Zhang, Liang Zhao
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Проблемы анализа, 2021 We consider a reproducing kernel radial Hilbert space of entire functions and prove the equivalence of several sufficient conditions for the existence of unconditional bases of reproducing kernels in such spaces.
K. P. Isaev, R. S. Yulmukhametov
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Mathematical Modelling and Analysis, 2021 The main aim of this paper is to present a hybrid scheme of both meshless Galerkin and reproducing kernel Hilbert space methods. The Galerkin meshless method is a powerful tool for solving a large class of multi-dimension problems.
Mehdi Mesrizadeh, Kamal Shanazari
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Analytic Kramer kernels, Lagrange-type interpolation series and de Branges spaces [PDF]
, 2011 The classical Kramer sampling theorem provides a method for obtaining orthogonal sampling formulas. In particular, when the involved kernel is analytic in the sampling parameter it can be stated in an abstract setting of reproducing kernel Hilbert spaces
Hernández-Medina, Miguel A. +7 more
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Some Hilbert spaces related with the Dirichlet space
Concrete Operators, 2016 We study the reproducing kernel Hilbert space with kernel kd , where d is a positive integer and k is the reproducing kernel of the analytic Dirichlet space.
Arcozzi Nicola +4 more
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Radial kernels and their reproducing kernel Hilbert spaces
Journal of Complexity, 2010 Let \(R\) be a continuous convex function on a Hilbert space \(H\). In learning theory, \[ A(\lambda):= \inf_{h\in H} \{\lambda\| h\|^2+ R(h)\}- \inf_{h\in H} R(h) \] is called an approximation error function. Here, \(H\) is a reproducing kernel Hilbert space (RKHS) of functions on \(\mathbb{R}^d\), i.e., such that the evaluations \(\delta_x: h\mapsto ...
Clint Scovel +3 more
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A novel method for fractal-fractional differential equations
Alexandria Engineering Journal, 2022 We consider the reproducing kernel Hilbert space method to construct numerical solutions for some basic fractional ordinary differential equations (FODEs) under fractal fractional derivative with the generalized Mittag–Leffler (M-L) kernel.
Nourhane Attia +4 more
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New characterizations of reproducing kernel Hilbert spaces and applications to metric geometry [PDF]
Opuscula Mathematica, 2021 We give two new global and algorithmic constructions of the reproducing kernel Hilbert space associated to a positive definite kernel. We further present a general positive definite kernel setting using bilinear forms, and we provide new examples.
Daniel Alpay, Palle E.T. Jorgensen
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