Universal approximation theorem for neural networks with inputs from a topological vector space
Machine Learning
2024-09-20 v1 Neural and Evolutionary Computing
Machine Learning
Abstract
We study feedforward neural networks with inputs from a topological vector space (TVS-FNNs). Unlike traditional feedforward neural networks, TVS-FNNs can process a broader range of inputs, including sequences, matrices, functions and more. We prove a universal approximation theorem for TVS-FNNs, which demonstrates their capacity to approximate any continuous function defined on this expanded input space.
Keywords
Cite
@article{arxiv.2409.12913,
title = {Universal approximation theorem for neural networks with inputs from a topological vector space},
author = {Vugar Ismailov},
journal= {arXiv preprint arXiv:2409.12913},
year = {2024}
}
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10 pages