Some comparisons of the worst-case errors in linear and neural network approximation
نویسندگان
چکیده
Worst-case errors in linear and neural-network approximation are investigated in a more general framework of fixed versus variable-basis approximation. Such errors are compared for balls in certain norms, tailored to sets of variablebasis functions. The tools for estimation of rates of variablebasis approximation are applied to sets of functions either computable by perceptrons with periodic or sigmoidal activations, or approximable with dimension-independent rates by one-hidden-layer networks with such perceptrons.
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