Approximation by superposition of sigmoidal and radial basis functions
Advances in Applied Mathematics
Neural networks for localized approximation
Mathematics of Computation
Spherical wavelets: efficiently representing functions on the sphere
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Spherical Marcinkiewicz-Zygmund inequalities and positive quadrature
Mathematics of Computation
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Local quadrature formulas on the sphere
Journal of Complexity
Essential rate for approximation by spherical neural networks
Neural Networks
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We introduce a class of zonal function network frames suitable for analyzing data collected at scattered sites on the surface of the unit sphere of a Euclidean space. Our frames consist of zonal function networks and are well localized. The frames belonging to higher and higher scale wavelet spaces have more and more vanishing polynomial moments. The main technique is applicable in the general setting of separable Hilbert spaces, in which context, we study the construction of new frames by perturbing an orthonormal basis.