Preprint available

[Radford Neal]

The following preprint is now available:

        Assessing Relevance Determination Methods Using DELVE

              Radford M. Neal, University of Toronto

  Empirically assessing the predictive performance of learning methods
  is an essential component of research in machine learning.  The DELVE
  environment was developed to support such assessments.  It provides a
  collection of datasets, a standard approach to conducting experiments
  with these datasets, and software for the statistical analysis of
  experimental results.  In this paper, DELVE is used to assess the
  performance of neural network methods when the inputs available to the
  network have varying degrees of relevance.  The results confirm that
  the Bayesian method of ``Automatic Relevance Determination'' (ARD) is
  often (but not always) helpful, and show that a variation on ``early
  stopping'' inspired by ARD is also beneficial.  The experiments also
  reveal some other interesting characteristics of the methods tested.
  This example illustrates the essential role of empirical testing, and
  shows the strengths and weaknesses of the DELVE environment.

  To appear in Generalization in Neural Networks and Machine Learning, 
    C. M. Bishop (editor), Springer-Verlag, 33 pages.

You can get this paper (in compressed Postscript) directly from:

  ftp://ftp.cs.toronto.edu/pub/radford/ard-delve.ps.Z

or via my home page (see URL below).

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Radford M. Neal                                       radford at cs.utoronto.ca
Dept. of Statistics and Dept. of Computer Science radford at utstat.utoronto.ca
University of Toronto                     http://www.cs.utoronto.ca/~radford
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