About this question
The universal approximation theorem states that a feed-forward neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function (provided some assumptions on the activation function are met).
Is there any other machine learning model (apart from any neural network model) that has been proved to be an universal function approximator (and that is potentially comparable to neural networks, in terms of usefulness and applicability)? If yes, can you provide a link to a research paper or book that shows the proof?
