When is there a representer theorem? Reflexive Banach spaces
DOI10.1007/s10444-021-09877-4OpenAlexW3185652123MaRDI QIDQ2045091
Publication date: 11 August 2021
Published in: Advances in Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1809.10284
kernel methodsregularisationreproducing kernel Banach spacesrepresenter theoremregularised interpolation
Learning and adaptive systems in artificial intelligence (68T05) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Convex functions, monotone operators and differentiability.
- Regularized learning in Banach spaces as an optimization problem: representer theorems
- Functional analysis, Sobolev spaces and partial differential equations
- On a kernel-based method for pattern recognition, regression, approximation, and operator inversion
- When is there a representer theorem? Nondifferentiable regularisers and Banach spaces
- Some results on Tchebycheffian spline functions and stochastic processes
- Asymptotic analysis of penalized likelihood and related estimators
- On the mathematical foundations of learning
- Frechet Differentiability of Lipschitz Functions and Porous Sets in Banach Spaces (AM-179)
- Just relax: convex programming methods for identifying sparse signals in noise
- Learning Theory
- Generalized Mercer Kernels and Reproducing Kernel Banach Spaces
- Multi-Valued Monotone Nonlinear Mappings and Duality Mappings in Banach Spaces
- Positivity of duality mappings
This page was built for publication: When is there a representer theorem? Reflexive Banach spaces