On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data
Machine Learning
2020-11-03 v1 Machine Learning
Abstract
A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is able to circumvent the curse of dimensionality.
Keywords
Cite
@article{arxiv.2011.00328,
title = {On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data},
author = {Michael Kohler and Adam Krzyzak},
journal= {arXiv preprint arXiv:2011.00328},
year = {2020}
}