Paper on Neocognitron Training avail on neuroprose

[David Lovell]

The following comment (3 pages in length) has been placed in the
Neuroprose archive and submitted to IEEE Transactions on Neural
Networks.  Any comments or questions (both of which are invited) should
be addressed to the first author: dlovell at s1.elec.uq.oz.au
Thanks must go to Jordan Pollack for maintaining this excellent service.
Apologies if this is the second copy of this notice to reach your site but
there were problems in mailing the original (ie. I don't think it got
through).

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   A NOTE ON A CLOSED-FORM TRAINING ALGORITHM FOR THE NEOCOGNITRON

			  David Lovell, Ah Chung Tsoi & Tom Downs
  Intelligent Machines Laboratory, Department of Electrical Engineering
         University of Queensland, Queensland 4072, Australia

In this note, a difficulty with the application of Hildebrandt's
closed-form training algorithm for the neocognitron is reported.  In
applying this algorithm we have observed that S-cells frequently fail
to respond to features that they have been trained to extract.  We
present results which indicate that this training vector rejection in
an important factor in the overall classification performance of the
neocognitron trained using Hildebrandt's procedure.

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filename: lovell.closed-form.ps.Z

FTP INSTRUCTIONS

     unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
     Name: anonymous
     Password: anything
     ftp> cd pub/neuroprose
     ftp> binary
     ftp> get lovell.closed-form.ps.Z
     ftp> bye
     unix% zcat lovell.closed-form.ps.Z | lpr
(or whatever *you* do to print a compressed PostScript file)
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David Lovell - dlovell at s1.elec.uq.oz.au  |
                                         |
Dept. Electrical Engineering             |
University of Queensland                 |
BRISBANE 4072                            |
Australia                                |
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tel: (07) 365 3564                       |