The mlp-mc-4 method uses a multilayer perceptron with one hidden layer
and NO direct input-output connections, trained by a Bayesian method
implemented using Markov chain Monte Carlo. The Automatic Relevance
Determination scheme is NOT used for determining the relevance of the
various inputs. The method can be used for both regression and
classification. Aside from the negatives emphasized above, it is the
same as mlp-mc-2.
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-mc-4 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.