Results 11 to 20 of about 11,485 (217)
A Characterization for reproducing kernel Hilbert spaces
Journal of Mathematical Analysis and Applications, 1974 AbstractLet G(t, s) be the Green's functions associated with N, a differential operator restricted to certain boundary conditions. Define (u, v)N = (Nu, v)L2. It is shown that the reproducing kernel Hilbert space generated by G is the same as the Hilbert-space completion with respect to ∥ · ∥N of the set of real valued functions which are in C2n and ...
Robert J. Grethel
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Reproducing kernels in separable Hilbert spaces [PDF]
Pacific Journal of Mathematics, 1968 H. Körezlioğlu
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A Primer on Reproducing Kernel Hilbert Spaces [PDF]
Foundations and Trends® in Signal Processing, 2015 Revised version submitted to Foundations and Trends in Signal ...
Jonathan H. Manton +1 more
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Integration in reproducing kernel Hilbert spaces of Gaussian kernels [PDF]
Mathematics of Computation, 2021 The Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from a numerical analysis standpoint. The basic problem of finding an algorithm for efficient numerical integration of functions reproduced by Gaussian kernels has not been fully solved.
Karvonen T, Oates CJ, Girolami M
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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 ...
Dan Timotin +3 more
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Reproducing kernel Hilbert space compactification of unitary evolution groups [PDF]
, 2018 D. Giannakis +2 more
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Benefits of Open Quantum Systems for Quantum Machine Learning
Advanced Quantum Technologies, EarlyView., 2023 Quantum machine learning (QML), poised to transform data processing, faces challenges from environmental noise and dissipation. While traditional efforts seek to combat these hindrances, this perspective proposes harnessing them for potential advantages. Surprisingly, under certain conditions, noise and dissipation can benefit QML.
María Laura Olivera‐Atencio +2 more
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Radial kernels and their reproducing kernel Hilbert spaces [PDF]
Journal of Complexity, 2010 AbstractWe describe how to use Schoenberg’s theorem for a radial kernel combined with existing bounds on the approximation error functions for Gaussian kernels to obtain a bound on the approximation error function for the radial kernel. The result is applied to the exponential kernel and Student’s kernel. To establish these results we develop a general
Ingo Steinwart +3 more
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Noncommutative reproducing kernel Hilbert spaces [PDF]
Journal of Functional Analysis, 2016 The theory of positive kernels and associated reproducing kernel Hilbert spaces, especially in the setting of holomorphic functions, has been an important tool for the last several decades in a number of areas of complex analysis and operator theory.
Joseph A. Ball +2 more
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