An unfeasability view of neural network learning
Machine Learning
2022-01-05 v1 Computational Complexity
Abstract
We define the notion of a continuously differentiable perfect learning algorithm for multilayer neural network architectures and show that such algorithms don't exist provided that the length of the data set exceeds the number of involved parameters and the activation functions are logistic, tanh or sin.
Keywords
Cite
@article{arxiv.2201.00945,
title = {An unfeasability view of neural network learning},
author = {Joos Heintz and Hvara Ocar and Luis Miguel Pardo and Andres Rojas Paredes and Enrique Carlos Segura},
journal= {arXiv preprint arXiv:2201.00945},
year = {2022}
}