Bounds on rates of variable-basis and neural-network approximation

Authors:
V. Kurkova;M. Sanguineti
Affiliations:
Inst. of Comput. Sci., Czechoslovak Acad. of Sci., Prague;-
Venue:
IEEE Transactions on Information Theory
Year:
2001

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Abstract

The 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 n elements of an orthonormal variable basis are derived