Title :
Kernel regression in HRBF networks for surface reconstruction
Author :
Bellocchio, F. ; Borghese, N.A. ; Ferrari, S. ; Piuri, V.
Author_Institution :
Dept. of Inf. Technol., Univ. of Milano, Crema
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.
Keywords :
image reconstruction; radial basis function networks; regression analysis; surface reconstruction; Kernel regression; hierarchical radial basis function network; neural network; surface reconstruction; Application software; Computer networks; Computer science; Conferences; Gaussian processes; Haptic interfaces; Information technology; Kernel; Solid modeling; Surface reconstruction; HRBF; Radial Basis Function Networks; kernel regression;
Conference_Titel :
Haptic Audio visual Environments and Games, 2008. HAVE 2008. IEEE International Workshop on
Conference_Location :
Ottawa, Ont.
Print_ISBN :
978-1-4244-2668-3
Electronic_ISBN :
978-1-4244-2669-0
DOI :
10.1109/HAVE.2008.4685317
