Results 21 to 30 of about 1,777,436 (249)
Factorizations of Kernels and Reproducing Kernel Hilbert Spaces [PDF]
Integral Equations and Operator Theory, 2017 The paper discusses a series of results concerning reproducing kernel Hilbert spaces, related to the factorization of their kernels. In particular, it is proved that for a large class of spaces isometric multipliers are trivial. One also gives for certain spaces conditions for obtaining a particular type of dilation, as well as a classification of ...
Kumari, Rani +3 more
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INFERENCE ON THE REPRODUCING KERNEL HILBERT SPACES
Universal Journal of Mathematics and Mathematical Sciences, 2021 Summary: Learning and interpolation are two extreme variants of the same problem, the object of which is to construct a function which is supposed to reasonably approximate an unknown function of which only a certain number of samples are known.
Agbokou, Komi, Mensah, Yaogan
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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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On functional reproducing kernels
Open Mathematics, 2023 We show that even if a Hilbert space does not admit a reproducing kernel, there could still be a kernel function that realizes the Riesz representation map.
Zhou Weiqi
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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces [PDF]
Journal of nonlinear science, 2017 Transfer operators such as the Perron–Frobenius or Koopman operator play an important role in the global analysis of complex dynamical systems. The eigenfunctions of these operators can be used to detect metastable sets, to project the dynamics onto the ...
Stefan Klus +2 more
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Reproducing Kernel Hilbert Spaces of Smooth Fractal Interpolation Functions
Fractal and Fractional, 2023 The theory of reproducing kernel Hilbert spaces (RKHSs) has been developed into a powerful tool in mathematics and has lots of applications in many fields, especially in kernel machine learning.
Dah-Chin Luor, Liang-Yu Hsieh
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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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Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis
IEEE Transactions on Signal and Information Processing over Networks, 2021 This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations.
V. Elias +3 more
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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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Optimal Control Problems: Convergence and Error Analysis in Reproducing Kernel Hilbert Spaces [PDF]
Control and Optimization in Applied Mathematics, 2021 In this article, we offer an efficient method to find an approximate solution for quadratic optimal control problems. The approximate solution is offered in a finite series form in reproducing kernel space.
Ebrahim Amini
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