Java software and TR available (competitive learning)

[Bernd Fritzke]

Dear connectionists,

this is to announce the availability of version 1.3 of the "DemoGNG"
Java applet and a new version of the accompanying technical report
draft "Some Competitive Learning Methods". The TR describes in detail
all methods implemented in DemoGNG as well as some others (such as
$k$-means and growing cell structures).

URLs and descriptions follow below.

Enjoy,
Bernd Fritzke and Hartmut Loos


URLs:
========
DemoGNG, for immediate execution:
http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html

DemoGNG, for download (972 kBytes):
ftp://ftp.neuroinformatik.ruhr-uni-bochum.de/pub/software/NN/DemoGNG/DemoGNG-1.3.tar.gz

TR, HTML:
http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/JavaPaper/

TR, Postscript, 45 pages, 376 kBytes:
ftp://ftp.neuroinformatik.ruhr-uni-bochum.de/pub/software/NN/DemoGNG/sclm.ps.gz


DemoGNG 1.3
========
DemoGNG is a Java applet which distributed as free software under the
GNU PUBLIC LICENSE and implements several methods related to
competitive learning. It is possible to experiment with the methods
using various (hardwired) data distributions and observe the learning
process. DemoGNG is highly interactive (e.g. dragging of neurons
during self-organization is possible) and has already been used for
neural network courses in several countries.

The following algorithms are now implemented:

(new)  LBG
       Hard Competitive Learning (constant and exponentially decaying
                                  learning rate)
       Neural Gas
       Competitive Hebbian Learning
       Neural Gas with Competitive Hebbian Learning
       Growing Neural Gas
(new)  Self-Organizing Map
(new)  Growing Grid

Features added since the previously released version include

 * display of Voronoi diagrams
 * display of Delaunay triangulations
 * additional probability distributions
 * a detailed manual
 * sound switched off by default 8v)

Draft Report
========
                  Some Competitive Learning Methods

                           Bernd Fritzke
                         Systems Biophysics
                  Institute for Neural Computation
                       Ruhr-Universit"at Bochum

  This report has the purpose of describing several algorithms from the
  literature all related to competitive learning. A uniform terminology
  is used for all methods. Moreover, identical examples are provided to
  allow a qualitative comparisons of the methods. The complete Java
  source code as well as a postscript version of this document may be
  accessed by ftp.

--
Bernd Fritzke * Institut f"ur Neuroinformatik             Tel. +49-234 7007845
Ruhr-Universit"at Bochum * Germany                        FAX. +49-234 7094210
WWW: http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/fritzke/top.html