Works about HILBERT space
Results: 5000
On the convergence of an implicit and relaxed Krasnoselskii-Mann type process for nonexpansive mappings.
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- Note di Matematica, 2024, v. 44, n. 2, p. 127, doi. 10.1285/i15900932v44n2p127
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Further inequalities for the numerical radii of Hilbert space operators.
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- Journal of Linear & Topological Algebra, 2024, v. 13, n. 4, p. 217
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- Article
Geometry of Numerical Range of Linear Operator Polynomial.
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- Chinese Annals of Mathematics, 2025, v. 46, n. 1, p. 151, doi. 10.1007/s11401-025-0008-1
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- Article
Essential Numerical Ranges of Linear Relations and Singular Discrete Linear Hamiltonian Systems.
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- Chinese Annals of Mathematics, 2025, v. 46, n. 1, p. 63, doi. 10.1007/s11401-025-0004-5
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- Article
Covariant projective representations of Hilbert–Lie groups.
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- Journal für die Reine und Angewandte Mathematik, 2025, v. 2025, n. 820, p. 1, doi. 10.1515/crelle-2024-0083
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An IID Test for Functional Time Series with Applications to High-Frequency VIX Index Data.
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- Risks, 2025, v. 13, n. 2, p. 25, doi. 10.3390/risks13020025
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The weighted Davis-Wielandt Berezin number for reproducing kernel Hilbert space operators.
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- Journal of Inequalities & Applications, 2025, v. 2025, n. 1, p. 1, doi. 10.1186/s13660-025-03255-0
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- Article
Asymptotic normality for kernel-based test of conditional mean independence in Hilbert space.
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- Mathematica Slovaca, 2025, v. 75, n. 1, p. 215, doi. 10.1515/ms-2025-0017
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- Article
Kernel Angle Dependence Measures in Metric Spaces.
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- Journal of Computational & Graphical Statistics, 2025, v. 34, n. 1, p. 280, doi. 10.1080/10618600.2024.2357620
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New iterative algorithms for solving split variational inclusions: New iterative algorithms for solving split variational inclusions: S. Dey et al.
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- Journal of Global Optimization, 2025, v. 91, n. 3, p. 587, doi. 10.1007/s10898-024-01444-7
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Extending Solutions and the Equations of Quantum Gravity Past the Big Bang Singularity.
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- Symmetry (20738994), 2025, v. 17, n. 2, p. 262, doi. 10.3390/sym17020262
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Low-Rank Representation of Reinforcement Learning Policies.
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- Journal of Artificial Intelligence Research, 2022, v. 75, p. 597, doi. 10.1613/jair.1.13854
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Optimal prediction for kernel-based semi-functional linear regression.
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- Analysis & Applications, 2024, v. 22, n. 3, p. 467, doi. 10.1142/S0219530523500318
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- Article
Cahn–Hilliard equations on random walk spaces.
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- Analysis & Applications, 2023, v. 21, n. 4, p. 959, doi. 10.1142/S0219530523500045
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Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings.
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- Analysis & Applications, 2023, v. 21, n. 3, p. 719, doi. 10.1142/S0219530522500142
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Dynamics of stochastic nonlocal reaction–diffusion equations driven by multiplicative noise.
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- Analysis & Applications, 2023, v. 21, n. 3, p. 597, doi. 10.1142/S0219530522500075
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Sparse representation of approximation to identity.
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- Analysis & Applications, 2022, v. 20, n. 4, p. 815, doi. 10.1142/S0219530521500251
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A mass-conserved tumor invasion system with quasi-variational degenerate diffusion.
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- Analysis & Applications, 2022, v. 20, n. 4, p. 615, doi. 10.1142/S0219530521500159
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- Article
Fractional Hardy-type and trace theorems for nonlocal function spaces with heterogeneous localization.
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- Analysis & Applications, 2022, v. 20, n. 3, p. 579, doi. 10.1142/S0219530521500329
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Balanced joint maximum mean discrepancy for deep transfer learning.
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- Analysis & Applications, 2021, v. 19, n. 3, p. 491, doi. 10.1142/S0219530520400035
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Reproducing kernel Hilbert spaces on manifolds: Sobolev and diffusion spaces.
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- Analysis & Applications, 2021, v. 19, n. 3, p. 363, doi. 10.1142/S0219530520400114
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Optimal rate for prediction when predictor and response are functions.
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- Analysis & Applications, 2020, v. 18, n. 4, p. 697, doi. 10.1142/S0219530520500037
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Superposition, reduction of multivariable problems, and approximation.
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- Analysis & Applications, 2020, v. 18, n. 5, p. 771, doi. 10.1142/S021953051941001X
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On the K-functional in learning theory.
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- Analysis & Applications, 2020, v. 18, n. 03, p. 423, doi. 10.1142/S0219530519500192
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Weighted p-regular kernels for reproducing kernel Hilbert spaces and Mercer Theorem.
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- Analysis & Applications, 2020, v. 18, n. 03, p. 359, doi. 10.1142/S0219530519500179
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Online regularized pairwise learning with least squares loss.
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- Analysis & Applications, 2020, v. 18, n. 1, p. 49, doi. 10.1142/S0219530519410070
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Convergence analysis of distributed multi-penalty regularized pairwise learning.
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- Analysis & Applications, 2020, v. 18, n. 1, p. 109, doi. 10.1142/S0219530519410045
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Analysis of regularized Nyström subsampling for regression functions of low smoothness.
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- Analysis & Applications, 2019, v. 17, n. 6, p. 931, doi. 10.1142/S0219530519500039
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Online minimum error entropy algorithm with unbounded sampling.
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- Analysis & Applications, 2019, v. 17, n. 2, p. 293, doi. 10.1142/S0219530518500148
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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ.
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- Analysis & Applications, 2019, v. 17, n. 1, p. 19, doi. 10.1142/S0219530518500203
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Regularized learning schemes in feature Banach spaces.
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- Analysis & Applications, 2018, v. 16, n. 1, p. 1, doi. 10.1142/S0219530516500202
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Learning rates for regularized least squares ranking algorithm.
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- Analysis & Applications, 2017, v. 15, n. 6, p. 815, doi. 10.1142/S0219530517500063
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Generalized Gramians: Creating frame vectors in maximal subspaces.
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- Analysis & Applications, 2017, v. 15, n. 1, p. 123, doi. 10.1142/S0219530516500019
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On extension theorems and their connection to universal consistency in machine learning.
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- Analysis & Applications, 2016, v. 14, n. 6, p. 795, doi. 10.1142/S0219530516400029
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Learning rates of regression with -norm loss and threshold.
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- Analysis & Applications, 2016, v. 14, n. 6, p. 809, doi. 10.1142/S0219530516400030
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Regularization schemes for minimum error entropy principle.
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- Analysis & Applications, 2015, v. 13, n. 4, p. 437, doi. 10.1142/S0219530514500110
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Reproducing kernel Hilbert spaces of CR functions for the Euclidean motion group.
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- Analysis & Applications, 2015, v. 13, n. 3, p. 331, doi. 10.1142/S021953051450047X
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Generalized sampling: From shift-invariant to U-invariant spaces.
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- Analysis & Applications, 2015, v. 13, n. 3, p. 303, doi. 10.1142/S0219530514500213
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A unified treatment for Tikhonov regularization using a general stabilizing operator.
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- Analysis & Applications, 2015, v. 13, n. 2, p. 201, doi. 10.1142/S0219530514500122
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INDEFINITE KERNEL NETWORK WITH DEPENDENT SAMPLING.
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- Analysis & Applications, 2013, v. 11, n. 5, p. -1, doi. 10.1142/S0219530513500206
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ON THE INCLUSION RELATION OF REPRODUCING KERNEL HILBERT SPACES.
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- Analysis & Applications, 2013, v. 11, n. 2, p. 1350014-1, doi. 10.1142/S0219530513500140
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VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY.
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- Analysis & Applications, 2010, v. 8, n. 1, p. 19, doi. 10.1142/S0219530510001503
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AN INFINITE DIMENSIONAL VERSION OF THE SCHUR CONVEXITY PROPERTY AND APPLICATIONS.
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- Analysis & Applications, 2007, v. 5, n. 2, p. 123, doi. 10.1142/S0219530507000912
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VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES OF INTEGRABLE FUNCTIONS AND MERCER THEOREM.
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- Analysis & Applications, 2006, v. 4, n. 4, p. 377, doi. 10.1142/S0219530506000838
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- Article
DISCRETIZATION ERROR ANALYSIS FOR TIKHONOV REGULARIZATION.
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- Analysis & Applications, 2006, v. 4, n. 1, p. 81, doi. 10.1142/S0219530506000711
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Estimating the Approximation Error in Learning Theory.
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- Analysis & Applications, 2003, v. 1, n. 1, p. 17, doi. 10.1142/S0219530503000089
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- Article
Kernel ensemble support vector machine with integrated loss in shared parameters space.
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- Multimedia Tools & Applications, 2023, v. 82, n. 12, p. 18077, doi. 10.1007/s11042-022-14226-8
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Maximum-Likelihood Asymptotic Inference for Autoregressive Hilbertian Processes.
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- Methodology & Computing in Applied Probability, 2015, v. 17, n. 1, p. 207, doi. 10.1007/s11009-013-9329-8
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Polytopes of Eigensteps of Finite Equal Norm Tight Frames.
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- Discrete & Computational Geometry, 2016, v. 56, n. 3, p. 727, doi. 10.1007/s00454-016-9799-x
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A Simple Proof of the Shallow Packing Lemma.
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- Discrete & Computational Geometry, 2016, v. 55, n. 3, p. 739, doi. 10.1007/s00454-016-9767-5
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