78 search results - page 1 / 16 » Radial Basis Function Networks and Complexity Regularization... |
NIPS
1996
13 years 6 months ago
1996
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
ICCV
2009
IEEE
13 years 2 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...
MVA
2000
13 years 6 months ago
2000
work, applies a nonlinear transformation from the input space to the hidden space. The output layer Partial face images, e.g.1 eyes, nose, and ear supplies the response of the netw...
ESANN
2003
13 years 6 months ago
2003
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
BMCBI
2006
13 years 5 months ago
2006
Background: The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for par...