Regularized learning schemes in feature Banach spaces
DOI10.1142/S0219530516500202zbMath1378.62015arXiv1410.6847OpenAlexW2963099468WikidataQ47037073 ScholiaQ47037073MaRDI QIDQ4594821
Patrick L. Combettes, Saverio Salzo, Silvia Villa
Publication date: 24 November 2017
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1410.6847
Banach spacesconsistencyregularizationreproducing kernelstatistical learningtotally convex functionempirical riskrepresenter theoremfeature map
Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Applications of functional analysis in probability theory and statistics (46N30) Probability theory on linear topological spaces (60B11)
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- Fast learning rate of multiple kernel learning: trade-off between sparsity and smoothness
- Two oracle inequalities for regularized boosting classifiers
- Solving support vector machines in reproducing kernel Banach spaces with positive definite functions
- Statistics for high-dimensional data. Methods, theory and applications.
- Optimal learning rates for least squares regularized regression with unbounded sampling
- Convex functions, monotone operators and differentiability.
- Regularized learning in Banach spaces as an optimization problem: representer theorems
- Convexity and well-posed problems
- Sparsity in penalized empirical risk minimization
- Kernel methods in machine learning
- Elastic-net regularization in learning theory
- On uniformly convex functionals
- Characteristic inequalities of uniformly convex and uniformly smooth Banach spaces
- Convex analysis and measurable multifunctions
- Iterative averaging of entropic projections for solving stochastic convex feasibility problems
- Totally convex functions for fixed points computation and infinite dimensional optimization
- Reproducing kernel Banach spaces with the \(\ell^1\) norm
- A distribution-free theory of nonparametric regression
- Sums and Gaussian vectors
- Weak convergence and empirical processes. With applications to statistics
- Vector-valued reproducing kernel Banach spaces with applications to multi-task learning
- Kernel regression with functional response
- Functional data analysis.
- Bregman distances, totally convex functions, and a method for solving operator equations in Banach spaces
- Continuity properties of projection operators
- Learning rates of least-square regularized regression
- Integral representation of dominated operations on spaces of continuous vector fields
- Local Rademacher complexities
- On the mathematical foundations of learning
- Accelerated and Inexact Forward-Backward Algorithms
- Spaces of Operator-valued Functions Measurable with Respect to the Strong Operator Topology
- Variational Analysis in Sobolev and BV Spaces
- Reproducing Kernel Banach Spaces with the ℓ1 Norm II: Error Analysis for Regularized Least Square Regression
- VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY
- VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES OF INTEGRABLE FUNCTIONS AND MERCER THEOREM
- Learning Theory
- Support Vector Machines
- Proximal Thresholding Algorithm for Minimization over Orthonormal Bases
- Inequalities in Banach spaces with applications
- Quantitative Stability of Variational Systems: I. The Epigraphical Distance
- Wavelet thresholding for some classes of non–Gaussian noise
- A family of minimax rates for density estimators in continuous time
- Learning Theory
- 10.1162/153244303321897690
- Viscosity Solutions of Minimization Problems
- Strong Convergence of Block-Iterative Outer Approximation Methods for Convex Optimization
- Regularization and Variable Selection Via the Elastic Net
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- On Learning Vector-Valued Functions
- Convex analysis and monotone operator theory in Hilbert spaces
- Introduction to nonparametric estimation
- On uniformly convex functions