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Tightness of bounds on rates of approximation by feedforward neural networks is investigated in a more general context of nonlinear approximation by variable-basis functions. Tight bounds on the worst case error in approximation by linear combinations of elements of an orthonormal variable basis are derived.Index Terms-Approximation by variable-basis functions, bounds on rates of approximation, complexity of neural networks, high-dimensional optimal decision problems.

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