Approximation by superposition of sigmoidal and radial basis functions
Advances in Applied Mathematics
Neural networks for localized approximation
Mathematics of Computation
Approximation and learning of convex superpositions
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A better approximation for balls
Journal of Approximation Theory
Geometry and topology of continuous best and near best approximations
Journal of Approximation Theory
Minimization of error functionals over perceptron networks
Neural Computation
Estimates of Data Complexity in Neural-Network Learning
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Hi-index | 0.00 |
It is shown that for any positive integer n and any function f in Lp([0,1]d) with p ∈ [1,∞) there exist n half-spaces such that f has a best approximation by a linear combination of their characteristic functions. Further, any sequence of linear combinations of n half-space characteristic functions converging in distance to the best approximation distance has a subsequence converging to a best approximation, i.e., the set of such n-fold linear combinations is an approximatively compact set.