Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Machine Learning
2024-05-14 v1 Machine Learning
Abstract
Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possible value is derived.
Keywords
Cite
@article{arxiv.2405.07619,
title = {Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent},
author = {Michael Kohler and Adam Krzyzak and Benjamin Walter},
journal= {arXiv preprint arXiv:2405.07619},
year = {2024}
}