Journal of Taibah University for Science, 2019 Reproducing kernel Hilbert space method is given for the solution of generalized Kuramoto–Sivashinsky equation. Reproducing kernel functions are obtained to get the solution of the generalized Kuramoto–Sivashinsky equation.
Ali Akgül, Ebenezer Bonyah
doaj +1 more source
Regularization in Reproducing Kernel Hilbert Spaces
, 2022 AbstractMethods for obtaining a functiongin a relationship$$y=g(x)$$y=g(x)from observed samples ofyandxare the building blocks for black-box estimation. The classical parametric approach discussed in the previous chapters uses a function model that depends on a finite-dimensional vector, like, e.g., a polynomial model.
Gianluigi Pillonetto +4 more
openaire +1 more source
On Gauge‐Invariant Entire Function Regulators and UV Finiteness in Non Local Quantum Field Theory
Annalen der Physik, Volume 538, Issue 4, April 2026.We regulate the theory with an entire function of the covariant operator F(□/M∗2)$F(\square /M^{2}_{*})$. In the perturbative vacuum this becomes a momentum‐space factor F(−p2/M∗2)$F(-p^{2}/M^{2}_{*})$ that exponentially damps high momenta, most transparent after Wick rotation, rendering loop integrals UV finite.
J. W. Moffat, E. J. Thompson
wiley +1 more source
Multikernel Adaptive Filters Under the Minimum Cauchy Kernel Loss Criterion
IEEE Access, 2019 The Cauchy loss has been successfully applied in robust learning algorithms in the presence of large outliers, but it may suffer from performance degradation in complex nonlinear tasks.
Wei Shi, Kui Xiong, Shiyuan Wang
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An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces. [PDF]
Stat Sin, 2023 Zhang T, Simon N.
europepmc +1 more source
A new mean-Berezin norm for operators in reproducing kernel Hilbert spaces
Journal of Inequalities and ApplicationsA functional Hilbert space is defined as the Hilbert space K $\mathcal{K}$ of complex-valued functions defined on a set Θ. In this space, the evaluation functionals ψ ε ( h ) = h ( ε ) $\psi _{\varepsilon}(h) = h(\varepsilon )$ , for ε ∈ Θ $\varepsilon ...
Mojtaba Bakherad
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Adaptive Supervised Learning on Data Streams in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. [PDF]
Stat, 2023 Wang H, Li Q, Liu Y.
europepmc +1 more source
Bicomplex Bergman spaces on bounded domains
, 2018 The bicomplex Bergman spaces are studied for any bounded bicomplex domain. Its Bergman kernel is computed in terms of the kernels of the complex projections of the domain.
Perez-Regalado, Cesar O. +1 more
core
SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces. [PDF]
Magn Reson Med, 2021 Ramos-Llordén G +5 more
europepmc +1 more source
Learning Reconstructive Embeddings in Reproducing Kernel Hilbert Spaces via the Representer Theorem
IEEE Open Journal of the Computer SocietyMotivated by the growing interest in representation learning approaches that uncover the latent structure of high-dimensional data, this work proposes new algorithms for reconstruction-based manifold learning within Reproducing-Kernel Hilbert Spaces ...
Enrique Feito-Casares +2 more
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