Special issue of Connection Science

[A.Sharkey@dcs.shef.ac.uk]

      ************ COMBINING NEURAL NETS ************


      CALL FOR PAPERS: Deadline February 14th 1996


Papers are sought for this special issue of Connection Science.


The aim of this special issue is to examine when, how, and why neural
nets should be combined.  The reliability of neural nets can be
increased through the use of both redundant and modular nets, (either
trained on the same task under differing conditions, or on different
subcomponents of a task).  Questions about the exploitation of
redundancy and modularity in the combination of nets, or estimators,
have both an engineering and a biological relevance, and include the
following:

* how best to combine the outputs of several nets.

* quantification of the benefits of combining.

* how best to create redundant nets that generalise differently (e.g.
active learning methods).

* how to effectively subdivide a task. 

* communication between neural net modules.

* increasing the reliability of nets.

* the use of neural nets for safety critical applications.

Special issue editor: 
Amanda Sharkey (Sheffield, UK)


Editorial Board:
Leo Breiman (Berkeley, USA)
Nathan Intrator (Brown, USA)
Robert Jacobs (Rochester, USA)
Michael Jordan (MIT, USA)
Paul Munro (Pittsburgh, USA)
Michael Perrone (IBM, USA)
David Wolpert (Santa Fe Institute, USA)

We solicit either theoretical or experimental papers on this topic.
Questions and submissions concerning this special issue should be sent
by February 14th 1996 to: 

Dr Amanda Sharkey,Department of Computer Science,Regent
Court,Portobello Street,University of Sheffield,Sheffield, S1
4DP,United Kingdom.

Email: amanda at dcs.shef.ac.uk