Geometric Upper Bounds on Rates of Variable-Basis Approximation

Authors:
V. Kurkova;M. Sanguineti
Affiliations:
Inst. of Comput. Sci., Acad. of Sci. of the Czech Republic, Prague;-
Venue:
IEEE Transactions on Information Theory
Year:
2008

Citing 0
Cited 8

Smooth Optimal Decision Strategies for Static Team Optimization Problems and Their Approximations

SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
A neural network of smooth hinge functions

IEEE Transactions on Neural Networks
Some comparisons of model complexity in linear and neural-network approximation

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Some comparisons of complexity in dictionary-based and linear computational models

Neural Networks
Bounds for approximate solutions of Fredholm integral equations using kernel networks

ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
2011 Special Issue: Can dictionary-based computational models outperform the best linear ones?

Neural Networks
Complexity estimates based on integral transforms induced by computational units

Neural Networks
Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure

SIAM Journal on Optimization

Quantified Score

Hi-index	754.84

Visualization

Abstract

In this paper, approximation by linear combinations of an increasing number n of computational units with adjustable parameters (such as perceptrons and radial basis functions) is investigated. Geometric upper bounds on rates of convergence of approximation errors are derived. The bounds depend on certain parameters specific for each function to be approximated. The results are illustrated by examples of values of such parameters in the case of approximation by linear combinations of orthonormal functions.