Adaptive Supervised Learning on Data Streams in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. [PDF]
Stat, 2023 Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring ...
Wang H, Li Q, Liu Y.
europepmc +2 more sources
Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods. [PDF]
PLoS ONEKernel methods are widely applied across various domains; however, structural limitations of reproducing kernels in Hilbert spaces pose significant challenges.
Yi Ding, Ying Zhao, Yan Pei
doaj +2 more sources
Reproducing kernel Hilbert spaces [PDF]
High-Dimensional Statistics, 2019 This chapter introduces an elegant mathematical theory that has been developed for nonparametric regression with penalized estimation.
Ronald Christensen
semanticscholar +3 more sources
Integration in reproducing kernel Hilbert spaces of Gaussian kernels [PDF]
Mathematics of Computation, 2020 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.
T. Karvonen, C. Oates, M. Girolami
semanticscholar +5 more sources
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces [PDF]
Analysis and Applications, 2019 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.
E. De Vito, Nicole Mücke, L. Rosasco
semanticscholar +5 more sources
Reproducing Kernel Hilbert Spaces and fractal interpolation
Journal of Computational and Applied Mathematics, 2011 The main result of this work is to link two fields: fractal interpolation and reproducing kernel Hilbert space. The corresponding spaces of the simple fractal interpolation functions are also reproducing kernel Hilbert spaces, as specific cases. The authors provide the elements for calculating the respective kernel functions for reproducing kernel ...
P Bouboulis
exaly +3 more sources
Safe exploration in reproducing kernel Hilbert spaces
CoRRPopular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded norm in a reproducing kernel Hilbert
Abdullah Tokmak +3 more
semanticscholar +5 more sources
An Operator Analysis on Stochastic Differential Equation (SDE)-Based Diffusion Generative Models [PDF]
EntropyScore-based generative models, grounded in stochastic differential equations (SDEs), excel in producing high-quality data but suffer from slow sampling due to the extensive nonlinear computations required for iterative score function evaluations.
Yunpei Wu, Yoshinobu Kawahara
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Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits. [PDF]
Genetics, 2008 Gianola D, van Kaam JB.
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On Relative Reproducing Kernel Banach Spaces: Definitions, Semi-Inner Product and Feature Maps [PDF]
Sahand Communications in Mathematical Analysis, 2023 In this paper, a special class of relative reproducing kernel Banach spaces a semi-inner product is studied. We extend the concept of relative reproducing kernel Hilbert spaces to Banach spaces. We present these relative reproducing kernel Banach spaces
Mohammadreza Foroutan
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