New preprint in neuroprose - avoiding local minima by homotopy

[D.Gorse@cs.ucl.ac.uk]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/gorse.homotopy.ps.Z

The file gorse.homotopy.ps.Z is now available for copying from the 
Neuroprose archive. This is a 6 page paper, submitted to WCNN '94
San Diego. A longer and more detailed paper describing this work is in
preparation and will be available soon.
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	    A CLASSICAL ALGORITHM FOR AVOIDING LOCAL MINIMA

		      D Gorse and A Shepherd
		  Department of Computer Science
	 University College, Gower Street, London WC1E 6BT, UK

			   J G Taylor
		   Department of Mathematics
	  King's College, Strand, London WC2R 2LS, UK

ABSTRACT:
Conventional methods of supervised learning are inevitably faced with the
problem of local minima; evidence is presented that conjugate gradient
and quasi-Newton techniques are particularly susceptible to being trapped
in sub-optimal solutions. A new classical technique is presented which by
the use of a homotopy on the range of the target outputs allows
supervised learning methods to find a global minimum of the error function
in almost every case.

Denise Gorse (D.Gorse at cs.ucl.ac.uk)
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To obtain a copy:

  ftp archive.cis.ohio-state.edu
  login: anonymous
  password: <your email address>
  cd pub/neuroprose
  binary
  get gorse.homotopy.ps.Z
  quit

Then at your system:

  uncompress gorse.homotopy.ps.Z
  lpr -P<printer-name> gorse.homotopy.ps