scientific article; zbMATH DE number 7626740
From MaRDI portal
Publication:5053232
Publication date: 6 December 2022
Full work available at URL: https://arxiv.org/abs/2006.14101
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Banach spacesparse learningrepresenter theoremregularized learningminimum-norm interpolationsemi-discrete inverse problem
Learning and adaptive systems in artificial intelligence (68T05) Geometry and structure of normed linear spaces (46B20) Abstract approximation theory (approximation in normed linear spaces and other abstract spaces) (41A65) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10)
Related Items (4)
Parameter choices for sparse regularization with the ℓ1 norm * ⋮ Sparse machine learning in Banach spaces ⋮ A duality approach to regularized learning problems in Banach spaces ⋮ Regularization method for the generalized moment problem in a functional reproducing kernel Hilbert space
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Solving support vector machines in reproducing kernel Banach spaces with positive definite functions
- Regularized learning in Banach spaces as an optimization problem: representer theorems
- Multiplicative noise removal in imaging: an exp-model and its fixed-point proximity algorithm
- Best \(L_ p\) approximation with multiple constraints for \(1\leq p<\infty\)
- On the characterization and computation of best monotone approximation in \(L_ p[0,1\) for \(1\leq p<\infty\)]
- Fitting a \(C^m\)-smooth function to data. II
- Constrained \(L_ p\) approximation
- A model for strategic planning under uncertainty
- Reproducing kernel Banach spaces with the \(\ell^1\) norm
- Proximal algorithms in statistics and machine learning
- Constrained best approximation in Hilbert space
- Dual cones, constrained \(n\)-convex, \(L_ p\)-approximation, and perfect splines
- Constrained \(L_ p\)-approximation by generalized \(n\)-convex functions induced by ECT-systems
- Constrained best approximation in Hilbert space III. Applications of \(n\)-convex functions
- Regularization networks and support vector machines
- Regularization in a functional reproducing kernel Hilbert space
- Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling
- Multi-task learning in vector-valued reproducing kernel Banach spaces with the \(\ell^1\) norm
- Impact of different types of non linearity on the oscillatory behavior of higher order neutral difference equations
- Inner-product quasilinear spaces with applications in signal processing
- Fixed-point proximity algorithm for minimal norm interpolation
- A unifying representer theorem for inverse problems and machine learning
- A content-adaptive unstructured grid based integral equation method with the TV regularization for SPECT reconstruction
- A two-step fixed-point proximity algorithm for a class of non-differentiable optimization models in machine learning
- Multi-step fixed-point proximity algorithms for solving a class of optimization problems arising from image processing
- A sharp form of Whitney's extension theorem
- Fitting a \(C^m\)-smooth function to data. III.
- Functional reproducing kernel Hilbert spaces for non-point-evaluation functional data
- A predual of \(l_1\) which is not isomorphic to a C(K) space
- Asymptotic analysis of penalized likelihood and related estimators
- On the mathematical foundations of learning
- Discrete reproducing kernel Hilbert spaces: Sampling and distribution of Dirac-masses
- Hilbert space embeddings and metrics on probability measures
- Representer Theorems for Sparsity-Promoting <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math> </inline-formula> Regularization
- Proximity algorithms for image models: denoising
- Reproducing Kernel Banach Spaces with the ℓ1 Norm II: Error Analysis for Regularized Least Square Regression
- Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction
- The approximate inverse in action: IV. Semi-discrete equations in a Banach space setting
- A proximity algorithm accelerated by Gauss–Seidel iterations for L1/TV denoising models
- Approximation inLp[0,1 byn-convex functions]
- Kernel techniques: From machine learning to meshless methods
- Fast collocation methods for solving ill-posed integral equations of the first kind
- Integral equation models for image restoration: high accuracy methods and fast algorithms
- An Introduction to Banach Space Theory
- Contemporary Computational Mathematics - A Celebration of the 80th Birthday of Ian Sloan
- A Primal Dual Active Set Algorithm With Continuation for Compressed Sensing
- Sparse Support Vector Machines in Reproducing Kernel Banach Spaces
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Learning Theory
- Shannon sampling and function reconstruction from point values
- A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
- Multiscale Methods for Fredholm Integral Equations
- Image restoration: Total variation, wavelet frames, and beyond
- A representer theorem for deep kernel learning
- Variations on a Theorem of Tate
- Generalized Mercer Kernels and Reproducing Kernel Banach Spaces
- Refinement of Operator-valued Reproducing Kernels
- A fast nonstationary iterative method with convex penalty for inverse problems in Hilbert spaces
- Classes of Semi-Inner-Product Spaces
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- Theory of Reproducing Kernels
- Generalized representer theorems in Banach spaces
- Convex analysis and monotone operator theory in Hilbert spaces
- Scattered Data Approximation
- Compressed sensing
- Scattered Data Approximation
- Best approximation in inner product spaces
- On the dual formulation of regularized linear systems with convex risks
This page was built for publication:
