Simultaneous \(\mathbf L^p\)-approximation order for neural networks
From MaRDI portal
Publication:2568010
DOI10.1016/j.neunet.2005.03.013zbMath1088.68158DBLPjournals/nn/XuC05OpenAlexW2017050618WikidataQ51443380 ScholiaQ51443380MaRDI QIDQ2568010
Publication date: 7 October 2005
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2005.03.013
Related Items (17)
Approximation theorems for a family of multivariate neural network operators in Orlicz-type spaces ⋮ Interpolation and rates of convergence for a class of neural networks ⋮ Lower estimation of approximation rate for neural networks ⋮ Construction and approximation for a class of feedforward neural networks with sigmoidal function ⋮ Analysis of convergence performance of neural networks ranking algorithm ⋮ Convergence for a family of neural network operators in Orlicz spaces ⋮ The errors of simultaneous approximation of multivariate functions by neural networks ⋮ Simultaneous Approximations of Polynomials and Derivatives and Their Applications to Neural Networks ⋮ Applications of the Bernstein-Durrmeyer operators in estimating the norm of Mercer kernel matrices ⋮ Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations ⋮ Estimates for the neural network operators of the max-product type with continuous and \(p\)-integrable functions ⋮ The estimation of simultaneous approximation order for neural networks ⋮ A way of constructing translation network operators by Bernstein operators ⋮ Hermite interpolation by neural networks ⋮ The essential order of simultaneous approximation for neural networks ⋮ Quantitative estimates involving K-functionals for neural network-type operators ⋮ Rates of approximation by neural network interpolation operators
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The essential order of approximation for neural networks
- On multivariate approximation by Bernstein-type polynomials
- Bernstein-Durrmeyer polynomials on a simplex
- Approximation by Ridge functions and neural networks with one hidden layer
- Approximation by superposition of sigmoidal and radial basis functions
- Multilayer feedforward networks are universal approximators
- Degree of approximation by neural and translation networks with a single hidden layer
- Simultaneous approximations of multivariate functions and their derivatives by neural networks with one hidden layer
- Improved rates and asymptotic normality for nonparametric neural network estimators
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Simultaneous \(\mathbf L^p\)-approximation order for neural networks
