IMAGE REGULARITY CONDITIONS BASED ON NONCONVEX SEPARATION WITH APPLICATIONS.
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- Journal of Nonlinear & Variational Analysis, 2025, v. 9, n. 4, p. 481, doi. 10.23952/jnva.9.2025.4.02
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Works matching DE "MATHEMATICAL regularization"
Results: 2494
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2
Variable selection in sparse multivariate GLARMA models: application to germination control by environment.
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- Statistical Methods & Applications, 2025, v. 34, n. 2, p. 291, doi. 10.1007/s10260-025-00786-0
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3
Two regularization methods for identifying the source term of Caputo–Hadamard type time fractional diffusion-wave equation.
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- Journal of Inverse & Ill-Posed Problems, 2025, v. 33, n. 3, p. 369, doi. 10.1515/jiip-2024-0051
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4
On penalty method for equilibrium problems in lexicographic order.
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- Positivity, 2018, v. 22, n. 1, p. 39, doi. 10.1007/s11117-017-0496-7
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5
Level sets relaxation, epigraphical relaxation and conditioning in optimization.
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- Positivity, 2015, v. 19, n. 4, p. 769, doi. 10.1007/s11117-015-0327-7
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6
Multi-consensus decentralized primal-dual fixed point algorithm for distributed learning.
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- Machine Learning, 2024, v. 113, n. 7, p. 4315, doi. 10.1007/s10994-024-06537-8
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7
Faster Riemannian Newton-type optimization by subsampling and cubic regularization.
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- Machine Learning, 2023, v. 112, n. 9, p. 3527, doi. 10.1007/s10994-023-06321-0
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8
On the benefits of representation regularization in invariance based domain generalization.
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- Machine Learning, 2022, v. 111, n. 3, p. 895, doi. 10.1007/s10994-021-06080-w
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9
Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion.
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- Machine Learning, 2020, v. 109, n. 3, p. 603, doi. 10.1007/s10994-019-05846-7
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10
Communication-efficient distributed multi-task learning with matrix sparsity regularization.
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- Machine Learning, 2020, v. 109, n. 3, p. 569, doi. 10.1007/s10994-019-05847-6
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11
Data scarcity, robustness and extreme multi-label classification.
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- Machine Learning, 2019, v. 108, n. 8/9, p. 1329, doi. 10.1007/s10994-019-05791-5
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12
On better training the infinite restricted Boltzmann machines.
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- Machine Learning, 2018, v. 107, n. 6, p. 943, doi. 10.1007/s10994-018-5696-2
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13
Online optimization for max-norm regularization.
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- Machine Learning, 2017, v. 106, n. 3, p. 419, doi. 10.1007/s10994-017-5628-6
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14
Sparse and low-rank matrix regularization for learning time-varying Markov networks.
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- Machine Learning, 2016, v. 105, n. 3, p. 335, doi. 10.1007/s10994-016-5568-6
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15
Rényi divergence minimization based co-regularized multiview clustering.
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- Machine Learning, 2016, v. 104, n. 2-3, p. 411, doi. 10.1007/s10994-016-5543-2
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16
Adaptive trajectory analysis of replicator dynamics for data clustering.
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- Machine Learning, 2016, v. 104, n. 2-3, p. 271, doi. 10.1007/s10994-016-5573-9
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- Article
17
Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee.
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- Machine Learning, 2016, v. 104, n. 2-3, p. 195, doi. 10.1007/s10994-016-5578-4
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18
Factorizing LambdaMART for cold start recommendations.
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- Machine Learning, 2016, v. 104, n. 2-3, p. 223, doi. 10.1007/s10994-016-5579-3
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19
Generalization bounds for metric and similarity learning.
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- Machine Learning, 2016, v. 102, n. 1, p. 115, doi. 10.1007/s10994-015-5499-7
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20
Additive regularization of topic models.
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- Machine Learning, 2015, v. 101, n. 1-3, p. 303, doi. 10.1007/s10994-014-5476-6
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21
Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm.
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- Machine Learning, 2015, v. 101, n. 1-3, p. 163, doi. 10.1007/s10994-014-5455-y
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- Article
22
Regularized feature selection in reinforcement learning.
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- Machine Learning, 2015, v. 100, n. 2-3, p. 655, doi. 10.1007/s10994-015-5518-8
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23
A generalized online mirror descent with applications to classification and regression.
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- Machine Learning, 2015, v. 99, n. 3, p. 411, doi. 10.1007/s10994-014-5474-8
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24
Learning with tensors: a framework based on convex optimization and spectral regularization.
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- Machine Learning, 2014, v. 94, n. 3, p. 303, doi. 10.1007/s10994-013-5366-3
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25
Adaptive regularization of weight vectors.
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- Machine Learning, 2013, v. 91, n. 2, p. 155, doi. 10.1007/s10994-013-5327-x
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26
Parallel blind deconvolution of astronomical images based on the fractal energy ratio of the image and regularization of the point spread function.
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- Experimental Astronomy, 2014, v. 38, n. 1/2, p. 41, doi. 10.1007/s10686-014-9405-2
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27
Low‐rank tensor completion with sparse regularization in a transformed domain.
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- Numerical Linear Algebra with Applications, 2021, v. 28, n. 6, p. 1, doi. 10.1002/nla.2387
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28
Hybrid enriched bidiagonalization for discrete ill‐posed problems.
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- Numerical Linear Algebra with Applications, 2019, v. 26, n. 3, p. N.PAG, doi. 10.1002/nla.2230
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29
Regularizing properties of a class of matrices including the optimal and the superoptimal preconditioners.
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- Numerical Linear Algebra with Applications, 2019, v. 26, n. 2, p. N.PAG, doi. 10.1002/nla.2225
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30
Regularization matrices for discrete ill‐posed problems in several space dimensions.
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- Numerical Linear Algebra with Applications, 2018, v. 25, n. 4, p. 1, doi. 10.1002/nla.2163
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31
An alternating direction method of multipliers for the solution of matrix equations arising in inverse problems.
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- Numerical Linear Algebra with Applications, 2018, v. 25, n. 4, p. 1, doi. 10.1002/nla.2123
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32
A variational approach for restoring images corrupted by noisy blur kernels and additive noise.
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- Numerical Linear Algebra with Applications, 2017, v. 24, n. 6, p. n/a, doi. 10.1002/nla.2100
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33
Estimation of atmospheric PSF parameters for hyperspectral imaging.
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- Numerical Linear Algebra with Applications, 2015, v. 22, n. 5, p. 795, doi. 10.1002/nla.1986
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34
A modified truncated singular value decomposition method for discrete ill-posed problems.
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- Numerical Linear Algebra with Applications, 2014, v. 21, n. 6, p. 813, doi. 10.1002/nla.1938
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35
Preconditioners for state-constrained optimal control problems with Moreau-Yosida penalty function.
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- Numerical Linear Algebra with Applications, 2014, v. 21, n. 1, p. 81, doi. 10.1002/nla.1863
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36
Comment on 'Preconditioning of matrices partitioned in 2 Π2 block form: Eigenvalue estimates and Schwarz DD for mixed FEM' by Owe Axelsson, Radim Blaheta Comment on 'Preconditioning of matrices partitioned in 2 Π2 block form: Eigenvalue estimates and Schwarz DD for mixed FEM' by Owe Axelsson, Radim Blaheta
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- Numerical Linear Algebra with Applications, 2013, v. 20, n. 3, p. 533, doi. 10.1002/nla.818
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37
A regularization technique for the XFEM: extension to finite deformations, inelastic material behaviour and multifield problems.
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- PAMM: Proceedings in Applied Mathematics & Mechanics, 2016, v. 16, n. 1, p. 153, doi. 10.1002/pamm.201610065
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38
A regularization approach for damage models based on a displacement gradient.
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- PAMM: Proceedings in Applied Mathematics & Mechanics, 2015, v. 15, n. 1, p. 151, doi. 10.1002/pamm.201510066
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39
Funnel control for electrical circuits.
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- PAMM: Proceedings in Applied Mathematics & Mechanics, 2014, v. 14, n. 1, p. 3, doi. 10.1002/pamm.201410002
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40
On the gradient enhancement of microplane damage models.
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- PAMM: Proceedings in Applied Mathematics & Mechanics, 2014, v. 14, n. 1, p. 155, doi. 10.1002/pamm.201410065
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41
On regularization in parameter-free shape optimization.
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- PAMM: Proceedings in Applied Mathematics & Mechanics, 2014, v. 14, n. 1, p. 785, doi. 10.1002/pamm.201410375
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42
A C<sup>0</sup> interior penalty discontinuous Galerkin method for fourth‐order total variation flow. I: Derivation of the method and numerical results.
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- Numerical Methods for Partial Differential Equations, 2019, v. 35, n. 4, p. 1458, doi. 10.1002/num.22359
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43
Reconstructing unknown nonlinear boundary conditions in a time‐fractional inverse reaction–diffusion–convection problem.
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- Numerical Methods for Partial Differential Equations, 2019, v. 35, n. 3, p. 976, doi. 10.1002/num.22334
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44
Analysis of the immersed boundary method for a finite element Stokes problem.
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- Numerical Methods for Partial Differential Equations, 2019, v. 35, n. 1, p. 181, doi. 10.1002/num.22296
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45
Numerical study of a conservative weighted compact difference scheme for the symmetric regularized long wave equations.
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- Numerical Methods for Partial Differential Equations, 2019, v. 35, n. 1, p. 60, doi. 10.1002/num.22285
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46
A computational method based on Hermite wavelets for two‐dimensional Sobolev and regularized long wave equations in fluids.
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- Numerical Methods for Partial Differential Equations, 2018, v. 34, n. 5, p. 1693, doi. 10.1002/num.22232
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47
Solving the burgers' and regularized long wave equations using the new perturbation iteration technique.
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- Numerical Methods for Partial Differential Equations, 2018, v. 34, n. 5, p. 1489, doi. 10.1002/num.22214
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48
A high accuracy minimally invasive regularization technique for navier-stokes equations at high reynolds number.
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- Numerical Methods for Partial Differential Equations, 2017, v. 33, n. 3, p. 814, doi. 10.1002/num.22124
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49
Image denoising using the Gaussian curvature of the image surface.
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- Numerical Methods for Partial Differential Equations, 2016, v. 32, n. 3, p. 1066, doi. 10.1002/num.22042
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50
Numerical solution for a parabolic obstacle problem with nonsmooth initial data.
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- Numerical Methods for Partial Differential Equations, 2014, v. 30, n. 5, p. 1740, doi. 10.1002/num.21893
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