Regularized greedy algorithms for network training with data noise
The aim of this paper is to construct a modified greedy algorithm applicable to an ill-posed function approximation problem in presence of data noise. The authors provide a detailed convergence analysis of the algorithm and discuss the choice of the iteration parameters. Applications to neural networks are presented.
scientific article; zbMATH DE number 2162512