DocumentCode
3526862
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
fYear
2008
fDate
18-19 Oct. 2008
Firstpage
160
Lastpage
165
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/HAVE.2008.4685317
Filename
4685317
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