demev1
x which sampled from a
Gaussian distribution, and a target variable t generated by
computing sin(2*pi*x) and adding Gaussian noise. A 2-layer
network with linear outputs is trained by minimizing a sum-of-squares
error function with isotropic Gaussian regularizer, using the scaled
conjugate gradient optimizer. The hyperparameters alpha and
beta are re-estimated using the function evidence. A graph
is plotted of the original function, the training data, the trained
network function, and the error bars.
evidence, mlp, scg, demard, demmlp1Copyright (c) Ian T Nabney (1996-9)