three new papers in neuroprose

[Bernd Fritzke]

         *** DO NOT FORWARD TO ANY OTHER LISTS ***

The following technical reports have been placed in the 
neuroprose directory (ftp instructions follow the abstracts).
For two of the TR's also hardcopies are available.
Instructions are at the end of the posting.

Comments and questions are welcome. 

Thanks to Jordan Pollack for maintaining the neuroprose archive.

-Bernd

 International Computer Science Institute
 1947 Center Street, Suite 600
 Berkeley, CA 94704-1105
 USA

------------------------------------------------------------


		 Growing Cell Structures -
	  A Self-organizing Network for Unsupervised
		and Supervised Learning *)

		       Bernd Fritzke

		       ICSI, Berkeley
			 TR-93-026

			 (34 pages)

*) submitted for publication

     We present  a   new   self-organizing  neural   network
model  having two variants. The first variant performs unsu-
pervised learning and can be used for   data  visualization,
clustering,   and  vector  quantization.  The main advantage
over existing approaches,  e.g.,  the  Kohonen feature  map,
is   the  ability of the model to automatically find a suit-
able network structure and size. This is achieved through  a
controlled   growth  process  which also includes occasional
removal of units.
     The  second  variant  of  the  model  is  a  supervised
learning  method  which  results from the combination of the
abovementioned self-organizing network with the radial basis
function  (RBF)  approach. In this model it is possible - in
contrast to earlier approaches - to perform the  positioning
of  the RBF units and the supervised training of the weights
in parallel. Therefore, the current classification error can
be  used  to  determine  where to insert new RBF units. This
leads to small networks which generalize very well.  Results
on   the   two-spirals  benchmark and a vowel classification
problem are presented which are  better  than  any   results
previously published.

------------------------------------------------------------


	  Vector Quantization with a Growing and
		  Splitting Elastic Net *)

		       Bernd Fritzke
		       ICSI, Berkeley

			 (6 pages)

	 *) to be presented at ICANN-93, Amsterdam

     A new vector quantization method is proposed which gen-
erates codebooks incrementally.  New vectors are inserted in
areas of the input vector space where the quantization error
is especially high until the desired number of codebook vec-
tors is reached.  A one-dimensional topological neighborhood
makes  it  possible to interpolate new vectors from existing
ones.  Vectors not contributing to  error  minimization  are
removed.   After the desired number of vectors is reached, a
stochastic approximation phase fine tunes the codebook.  The
final  quality of the codebooks is exceptional. A comparison
with two well-known methods for vector quantization was per-
formed  by solving an image compression problem. The results
indicate that the new method is  significantly  better  than
both other approaches.

------------------------------------------------------------


    Kohonen Feature Maps and Growing Cell Structures --
		a Performance Comparison *)

		       Bernd Fritzke
		       ICSI, Berkeley

			 (8 pages)

*) to appear in Advances in  Neural  Information  Processing
Systems  5  C.L.  Giles, S.J. Hanson, and J.D. Cowan (eds.),
Morgan Kaufmann, San Mateo, CA, 1993

     A performance comparison of  two  self-organizing  net-
works,  the  Kohonen  Feature  Map and the recently proposed
Growing Cell Structures is made.  For this  purpose  several
performance  criteria  for self-organizing networks are pro-
posed and motivated.  The models are tested with three exam-
ple  problems  of increasing difficulty. The Kohonen Feature
Map demonstrates slightly superior results only for the sim-
plest  problem.   For the other more difficult and also more
realistic problems the Growing Cell Structures exhibit  sig-
nificantly  better  performance  by  every criterion.  Addi-
tional advantages of the new model are that  all  parameters
are constant over time and that size as well as structure of
the network are determined automatically.


************************* ftp instructions **********************

If you have the Getps script

  unix> Getps fritzke.tr93-26.ps.Z
  unix> Getps fritzke.icann93.ps.Z
  unix> Getps fritzke.nips92.ps.Z

  (Getps ftp's the named file, decompresses it, and asks wether to
   print it)

otherwise do first the following (to get Getps)

  unix> ftp archive.cis.ohio-state.edu       (or ftp 128.146.8.52)
  Connected to archive.cis.ohio-state.edu.
  220 archive.cis.ohio-state.edu FTP server ready.
  Name: anonymous
  331 Guest login ok, send ident as password.
  Password:<type your email address here>
  230 Guest login ok, access restrictions apply.
  ftp> cd pub/neuroprose
  250 CWD command successful.
  ftp> get Getps
  200 PORT command successful.
  150 Opening BINARY mode data connection for Getps (2190 bytes).
  226 Transfer complete.
  ftp> quit
  221 Goodbye.

************************* hardcopies ****************************

The NIPS92 paper and the 34-page paper have appeared as ICSI
technical  reports  TR-93-025  and  TR-93-026, respectively.
Hardcopies are available for a small charge for postage  and
handling.

For details please contact Vivian Balis (balis at icsi.berkeley.edu) 
at ICSI.