Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions
From MaRDI portal
Publication:5378401
DOI10.1080/01630563.2019.1605523zbMath1491.41008OpenAlexW2942680951MaRDI QIDQ5378401
Publication date: 12 June 2019
Published in: Numerical Functional Analysis and Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/01630563.2019.1605523
Applications of functional analysis in biology and other sciences (46N60) Neural networks for/in biological studies, artificial life and related topics (92B20) Multidimensional problems (41A63) Spaces of measures (46E27) Approximation by other special function classes (41A30)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Approximation by neural networks with scattered data
- Approximation by neural networks with weights varying on a finite set of directions
- Interpolation on lines by ridge functions
- A note on the representation of continuous functions by linear superpositions
- Approximation by a sum of two algebras. The lightning bolt principle
- Jackson-type inequalities for spherical neural networks with doubling weights
- Approximation by Ridge functions and neural networks with one hidden layer
- Optimal reconstruction of a function from its projections
- Lower bounds for approximation by MLP neural networks
- Ridgelets: estimating with ridge functions
- Saturation classes for MAX-product neural network operators activated by sigmoidal functions
- Interpolation by Ridge functions
- Degree of approximation by neural and translation networks with a single hidden layer
- On the approximation by single hidden layer feedforward neural networks with fixed weights
- Characterization of an extremal sum of ridge functions
- On the representation by linear superpositions
- On error formulas for approximation by sums of univariate functions
- On the approximation of a function of several variables by the sum of functions of fewer variables
- Convergence for a family of neural network operators in Orlicz spaces
- Approximation by ridge functions and neural networks with a bounded number of neurons
- Uniform approximation by real functions
- Approximation by Solutions of the Planar Wave Equation
- Extension of localised approximation by neural networks
- Approximation by Ridge Functions and Neural Networks
- Universal approximation bounds for superpositions of a sigmoidal function
- Ridge Functions
- On the proximinality of ridge functions
- On functions of three variables
- Measure Theoretic Results for Approximation by Neural Networks with Limited Weights
- A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions