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Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs

https://doi.org/10.1007/s00365-012-9153-3
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Outgoing claims: the papers this work cites, builds on, or argues with.

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The covering number in learning theory
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Coherent States, Wavelets and Their Generalizations
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Reproducing Kernels and their Applications
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A Course in Functional Analysis
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Eigenvalue Distribution of Compact Operators
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Reproducing Kernel Spaces and Realizations
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An Introduction to Sobolev Spaces and Interpolation Spaces
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Perturbation Theory for Linear Operators
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Topics in the Theory of Lifting
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Hilbertsche Räume mit Kernfunktion
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Theory of Reproducing Kernels and Applications
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Average-Case Analysis of Numerical Problems
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Learning Theory Estimates via Integral Operators and Their Approximations
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On Early Stopping in Gradient Descent Learning
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Model Selection for Regularized Least-Squares Algorithm in Learning Theory
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Optimal Rates for the Regularized Least-Squares Algorithm
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Liftings
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Mercer theorem for RKHS on noncompact sets
inferredcites →https://doi.org/10.1016/s0079-8169(08)x6053-2openalex-snapshot
Interpolation of Operators
inferredcites →https://doi.org/10.1017/9781108564175.026openalex-snapshot
Support Vector Machines
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Prediction, Learning, and Games
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Scattered Data Approximation
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Learning Theory
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Hilbertsche Räume mit Kernfunktion
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Pick Interpolation and Hilbert Function Spaces
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Theory of reproducing kernels
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On the mathematical foundations of learning
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On the Eigenspectrum of the Gram Matrix and the Generalization Error of Kernel-PCA
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Spline Models for Observational Data
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ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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Measure Theory and Integration
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Regularization in kernel learning
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Measure and Integration Theory
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MEASURE THEORY AND INTEGRATION
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Fast Rates for Regularized Least-Squares Algorithm
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Spline Models for Observational Data.
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A Course in Functional Analysis.
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Support Vector Machines
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Linear Information
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Learning with Kernels

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Small sample spaces for Gaussian processes
inferred← citeshttps://doi.org/10.3150/23-bej1639openalex-snapshot
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures
inferred← citeshttps://doi.org/10.3150/23-bej1647openalex-snapshot
Gaussian Whittle–Matérn fields on metric graphs
inferred← citeshttps://doi.org/10.3150/23-bej1653openalex-snapshot
Functional linear quantile regression on a two-dimensional domain
inferred← citeshttps://doi.org/10.3150/24-bej1836openalex-snapshot
Sequential kernel embedding for mediated and time-varying dose response curves
inferred← citeshttps://doi.org/10.3150/25-bej1859openalex-snapshot
Minimax optimal goodness-of-fit testing with kernel Stein discrepancy
inferred← citeshttps://doi.org/10.3233/jifs-169082openalex-snapshot
Ranking based ontology scheming using eigenpair computation
inferred← citeshttps://doi.org/10.3233/jifs-169367openalex-snapshot
Ontology learning algorithm for similarity measuring and ontology mapping using linear programming
inferred← citeshttps://doi.org/10.3390/aerospace13050468openalex-snapshot
Sparse-Grid Gaussian Kernel Quadrature Kalman Filter for Nonlinear State Estimation
inferred← citeshttps://doi.org/10.3390/axioms13110795openalex-snapshot
Intrinsic Functional Partially Linear Poisson Regression Model for Count Data
inferred← citeshttps://doi.org/10.3390/su14063387openalex-snapshot
Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing
inferred← citeshttps://doi.org/10.3929/ethz-b-000455329openalex-snapshot
Regularity and convergence analysis in Sobolev and Hölder spaces for generalized Whittle–Matérn fields
inferred← citeshttps://doi.org/10.3934/cpaa.2020178openalex-snapshot
Convergence of online pairwise regression learning with quadratic loss
inferred← citeshttps://doi.org/10.48550/arxiv.1308.1359openalex-snapshot
Invariances of random fields paths, with applications in Gaussian Process Regression
inferred← citeshttps://doi.org/10.48550/arxiv.1402.4501openalex-snapshot
A Kernel Independence Test for Random Processes
inferred← citeshttps://doi.org/10.48550/arxiv.1409.6008openalex-snapshot
On ANOVA decompositions of kernels and Gaussian random field paths
inferred← citeshttps://doi.org/10.48550/arxiv.1410.2392openalex-snapshot
Control functionals for Monte Carlo integration
inferred← citeshttps://doi.org/10.48550/arxiv.1411.2066openalex-snapshot
Learning Theory for Distribution Regression
inferred← citeshttps://doi.org/10.48550/arxiv.1608.03339openalex-snapshot
Distributed learning with regularized least squares
inferred← citeshttps://doi.org/10.48550/arxiv.1702.07254openalex-snapshot
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm
inferred← citeshttps://doi.org/10.48550/arxiv.1705.10182openalex-snapshot
Fast learning rate of deep learning via a kernel perspective
inferred← citeshttps://doi.org/10.48550/arxiv.1802.07528openalex-snapshot
Learning Integral Representations of Gaussian Processes
inferred← citeshttps://doi.org/10.48550/arxiv.1807.02582openalex-snapshot
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
inferred← citeshttps://doi.org/10.48550/arxiv.1808.07280openalex-snapshot
On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance
inferred← citeshttps://doi.org/10.48550/arxiv.1811.10809openalex-snapshot
Koopman Theory and Linear Approximation Spaces
inferred← citeshttps://doi.org/10.48550/arxiv.1911.05350openalex-snapshot
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
inferred← citeshttps://doi.org/10.48550/arxiv.2002.09677openalex-snapshot
Kernel interpolation with continuous volume sampling
inferred← citeshttps://doi.org/10.48550/arxiv.2003.00306openalex-snapshot
Dimension-free convergence rates for gradient Langevin dynamics in RKHS
inferred← citeshttps://doi.org/10.48550/arxiv.2008.11095openalex-snapshot
A Kernel Two-Sample Test for Functional Data
inferred← citeshttps://doi.org/10.48550/arxiv.2010.12842openalex-snapshot
Stochastic Gradient Descent Meets Distribution Regression
inferred← citeshttps://doi.org/10.48550/arxiv.2102.00760openalex-snapshot
Fast rates in structured prediction
inferred← citeshttps://doi.org/10.48550/arxiv.2105.07446openalex-snapshot
Sobolev Norm Learning Rates for Conditional Mean Embeddings
inferred← citeshttps://doi.org/10.48550/arxiv.2106.08750openalex-snapshot
Quasi-Bayesian Dual Instrumental Variable Regression
inferred← citeshttps://doi.org/10.48550/arxiv.2106.14277openalex-snapshot
How many moments does MMD compare?
inferred← citeshttps://doi.org/10.48550/arxiv.2110.12798openalex-snapshot
Variational Gaussian Processes: A Functional Analysis View
inferred← citeshttps://doi.org/10.48550/arxiv.2310.09437openalex-snapshot
Signal reconstruction using determinantal sampling
inferred← citeshttps://doi.org/10.5463/thesis.1183openalex-snapshot
Asymptotics for variational Gaussian processes
inferred← citeshttps://doi.org/10.5705/ss.202022.0180openalex-snapshot
Functional-Input Gaussian Processes with Applications to Inverse Scattering Problems
inferred← citeshttps://doi.org/10.5705/ss.202022.0272openalex-snapshot
Efficient Learning of Nonparametric Directed Acyclic Graph With Statistical Guarantee
inferred← citeshttps://doi.org/10.70675/7153dc52z3c48z4a31z8b7bzd0b3fa4a7b66openalex-snapshot
From weakly supervised learning to active labeling
inferred← citeshttps://doi.org/10.70675/b71bb1bbza8d5z4610zab4bz538c19aa64d1openalex-snapshot
Subspace sampling using determinantal point processes.
inferred← citeshttps://doi.org/10.70675/c34ea874z93eaz4e35za730zd2a93dee9702openalex-snapshot
Learning function-valued functions in reproducible kernel Hilbert spaces with integral losses : Application to infinite task learning
inferred← citeshttps://doi.org/10.7736/kspe.2018.35.2.163openalex-snapshot
Feature-Based Analysis for Fault Diagnosis of Gas Turbine using Machine Learning and Genetic Algorithms
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