We present a Regularization Network approach based on Kolmogorovs superposition theorem (KST) to reconstruct higher dimensional continuous functions from their function values on discrete data points. The ansatz is based on a new constructive proof of a version of the theorem. Additionally, the thesis gives a comprehensive overview on the various versions of KST that exist and its relation to well known approximation schemes and Neural Networks. The efficient representation of higher dimensional continuous functions as superposition of univariate continuous functions suggests the conjecture...
We present a Regularization Network approach based on Kolmogorovs superposition theorem (KST) to reconstruct higher dimensional continuous functions f...
