Radford Neal's Research: Evaluation of Learning Methods
Many methods for "learning" relationships from data have been
proposed, some based on new ideas such as neural networks, other
building on traditional statistical methods. Good empirical
comparisons of these methods are scarce. I was involved in the
DELVE project,
which hoped to remedy this situation by providing a good collection of
datasets for testing learning procedures, a well-designed environment
in which valid tests can be conducted, and an archive of test results
for many methods. This project is now largely in abayence.
The following paper demonstrates how DELVE can be used to address
interesting research questions:
Neal, R. M. (1998) ``Assessing relevance determination methods using DELVE'',
in C. M. Bishop (editor), Neural Networks and Machine Learning,
pp. 97-129, Springer-Verlag:
abstract,
associated references,
postscript, pdf.
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