139 search results - page 6 / 28 » Numerical differentiation by radial basis functions approxim... |
ICCV
2009
IEEE
13 years 5 months ago
2009
IEEE
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
ESANN
1998
13 years 9 months ago
1998
In the present paper, Wavelet Networks, are proven to be, as well as many other neural paradigms, a speci c case of the generic paradigm named Weighted Radial Basis Functions Netw...
ESANN
2007
13 years 9 months ago
2007
Abstract. Using radial basis function networks for function approximation tasks suffers from unavailable knowledge about an adequate network size. In this work, a measuring techni...
ICANN
2007
Springer
14 years 1 months ago
2007
Springer
Input selection in the nonlinear function approximation is important and difficult problem. Neural networks provide good generalization in many cases, but their interpretability is...
ISBI
2004
IEEE
14 years 8 months ago
2004
IEEE
We describe the use of compact support radial basis functions (CSRBFs) for simulation of soft tissue deformation. CSRBFs allow surface and volumetric deformations to be computed i...