The mlp-bgd-3 method does regression using a multilayer perceptron
neural network with one hidden layer, trained by batch gradient
descent, with learning rates out of inputs set statically based on the
correlations with the target. Aside from this scheme for setting
learning rates, it operates the same as mlp-bgd-1.
See the notes for more details.
This method uses the software for flexible Bayesian modeling written
by Radford Neal (release of 1997-06-22), available from
Radford Neal's home page.
Directory listing of the results (and source
files) available for the mlp-bgd-3 method. Put the desired files
in the appropriate methods directory in your delve hierarchy and
uncompress them with using the "gunzip *.gz" command and
untar them using "tar -xvf *.tar".
Neal, R. M. (1998) ``Assessing relevance determination methods using DELVE'',
to appear in C. M. Bishop (ed) Generalization in Neural Networks and
Machine Learning, Springer-Verlag.