Method home for mlp-bgd-2

The mlp-bgd-2 method does regression and classification using a multilayer perceptron neural network with one hidden layer, trained by batch gradient descent, with learning rates out of inputs dynamically adjusted based on the fourth power of the gradient. Aside from this adjustment, 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-2 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".

Related References

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.
Last Updated 20 May 1998
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