The Hierarchical Radial Basis Function (HRBF) Network is a neural model that proved its suitability in the surface reconstruction problem. Its non-iterative configuration algorithm requires an estimate of the surface in the centers of the units of the network. In this paper, we analyze the effect of different estimators in training HRBF networks, in terms of accuracy, required units, and computational time.
Kernel Regression in HRBF Networks for Surface Reconstruction / F. Bellocchio, N.A. Borghese, S. Ferrari, V. Piuri - In: Haptic Audio visual Environments and Games, 2008 : HAVE 2008 : IEEE International Workshop on[s.l] : IEEE, 2008 Oct. - ISBN 978-1-4244-2668-3. - pp. 160-165 (( convegno HAVE-IEEE International Workshop on Haptic, Audio and Visual Environments and Games tenutosi a Ottawa nel 2008 [10.1109/HAVE.2008.4685317].
Kernel Regression in HRBF Networks for Surface Reconstruction
F. BellocchioPrimo
;N.A. BorgheseSecondo
;S. FerrariPenultimo
;V. PiuriUltimo
2008
Abstract
The Hierarchical Radial Basis Function (HRBF) Network is a neural model that proved its suitability in the surface reconstruction problem. Its non-iterative configuration algorithm requires an estimate of the surface in the centers of the units of the network. In this paper, we analyze the effect of different estimators in training HRBF networks, in terms of accuracy, required units, and computational time.
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