Learning Article

[Steve Minton]

Readers of this group may be interested in the following article, which
was just published in the Journal of Artificial Intelligence Research
(a journal which is available both online and in print).

Buntine, W.L. (1994)
  "Operations for Learning with Graphical Models", Volume 2, pages 159-225
   Postscript: volume2/buntine94a.ps (1.53M)
               compressed, volume2/buntine94a.ps.Z (568K)

  Abstract: This paper is a multidisciplinary review of empirical,
  statistical learning from a graphical model perspective.  Well-known
  examples of graphical models include Bayesian networks, directed
  graphs representing a Markov chain, and undirected networks
  representing a Markov field.  These graphical models are extended to
  model data analysis and empirical learning using the notation of
  plates.  Graphical operations for simplifying and manipulating a
  problem are provided including decomposition, differentiation, and the
  manipulation of probability models from the exponential family.  Two
  standard algorithm schemas for learning are reviewed in a graphical
  framework: Gibbs sampling and the expectation maximization algorithm.
  Using these operations and schemas, some popular algorithms can be
  synthesized from their graphical specification.  This includes
  versions of linear regression, techniques for feed-forward networks,
  and learning Gaussian and discrete Bayesian networks from data.  The
  paper concludes by sketching some implications for data analysis and
  summarizing how some popular algorithms fall within the framework
  presented.
  
  The main original contributions here are the decomposition techniques
  and the demonstration that graphical models provide a framework for
  understanding and developing complex learning algorithms.


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