The tightness of bounds on rates of approximation by feedforward neural networks is
investigated in a more general context of nonlinear approximation by variable-basis …

This paper presents a computationally efficient algorithm for function approximation with
piecewise linear sigmoidal nodes. A one hidden layer network is constructed one node at a …

The problem of approximating functions by neural networks using incremental algorithms is
studied. For functions belonging to a rather general class, characterized by certain …

In contrast to linear schemes, nonlinear approximation techniques allow for dimension
independent rates of convergence. Unfortunately, typical algorithms (such as, eg …

Results concerning the approximation rates of neural networks are of particular interest to
engineers. The results reported in the literature have" slow approximation rates" O (1//spl …

The aim of this paper is to construct a modified greedy algorithm applicable for an ill-posed
function approximation problem in presence of data noise. We provide a detailed …

In this paper we study the theoretical limits of finite constructive convex approximations of a
given function in a Hilbert space using elements taken from a reduced subset. We also …

In this paper we study the error bounds of constructive approximations of a given function
with elements taken from a prescribed dictionary or subspace. The paper contributes to …

This paper presents computationally e cient algorithms for function approximation with
hinging hyperplanes. Approximant units are added one at a time using the method of tting …

In learning theory the goal is to reconstruct a function defined on some (typically high
dimensional) domain Ω, when only noisy values of this function at a sparse, discrete subset …