Competitive Learning Methods   


This applet, DemoGNG, implements several methods related to competitive learning. It is possible to experiment with the methods using various data distributions and observe the learning process. A common terminology is used to make it easy to compare different methods. Hopefully, experimentation with the models will increase one's intuitive understanding and make it easier to judge their particular strengths and weaknesses. Here is a link on the updated version of the applet.

The following algorithms are available:


The original applet was written by Hartmut S. Loos and Bernd Fritzke and slightly modified by Olivier Michel and Sébastien Baehni.


DemoGNG (Version 1.3)

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How to use the applet
Tutorial: Some Competitive Learning Methods (Bernd Fritzke)


  1. Hard Competitive Learning: explain the behavior of the iterative LGB method (K-means). Test this algorithm on the ring distribution and on the HiLo density.
  2. Kohonen (Self Organizing Map): what are the principles of this algorithm? Test it with the ring distribution and the HiLo density. Explain the experimental results according to the theory.
  3. Configure the Kohonen algorithm with a grid size of 1x30 and choose the rectangular shape. What is the role of the sigmaf parameter ? What is the value of the sigmaf parameter for which the representation changes from a straight line to an oscillatory line ?