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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Approximation of functions continuous on compact sets by layered neural networks
 Fojtík, Vít ; Hakl, František (advisor) ; Mrázová, Iveta (referee)
Despite abundant research into neural network applications, many areas of the under- lying mathematics remain largely unexplored. The study of neural network expressivity is vital for understanding their capabilities and limitations. However, even for shallow networks this topic is far from solved. We provide an upper bound on the number of neurons of a shallow neural network required to approximate a function continuous on a compact set with given accuracy. Dividing the compact set into small polytopes, we ap- proximate the indicator function of each of them by a neural network and combine these into an approximation of the target function. This method, inspired by a specific proof of the Stone-Weierstrass Theorem, is more general than previous bounds of this character, with regards to approximation of continuous functions. Also, it is purely constructive. 1
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