Michael Biehl: A collection of no-nonsense GMLVQ demo code

A (hopefully) easy-to-use collection of 'no-nonsense' demo code
in Matlab (TM) is available for Generalized Matrix Relevance Learning (GMLVQ).
The zip-archives contain implementations of batch gradient descent training
with automated step size control for the simplest
variants of GMLVQ, procedures for validation and discriminative visualiation, a brief documentation,
and example data sets.
It is recommended to use the latest versions only, previous versions are provided
for completeness and reproducibility of earlier results.

Version 3.1, October 2021 (developed and tested in Matlab R2021a and R2021b *)
c/o Roland Veen, code restructured, several extensions and fixes, see documentation for details
(* should also work with earlier versions, but this has not been tested explicitly)

Version 3.0, March 2019 (developed and tested in Matlab R2019b *)
c/o Floris Westermann, code restructured, several extensions and fixes, see documentation for details
(* might work with Matlab 2017 versions, but has not been tested)

Version 2.3, January 2017 (works with Matlab R2014b *)
       zip-archive-2.3    bug fixed in averaging of prototypes over runs (**), see also documentation and code
(* might work with earlier and slightly later versions, not tested)

Version 2.2, April 2016:            zip-archive-2.2    improved computation of ROC (*,**), see documentation and code
Version 2.1, August 2015:        zip-archive-2.1    (see documentation, readme and disclaimer)

Feel free to use the code for getting acquainted with the method, but please do cite the link and papers, if appropriate.
In any case, do not use the code for critical applications and make sure to read the disclaimer.
In the very likely case that you should come across bugs or would like to comment or suggest improvements, please let me know.
Do not expect much of the code in terms of readability and efficiency.
If you would like to keep receiving update notifications, please send an e-mail to m.biehl@rug.nl

Also note that a more complete and more sophisticated Matlab (TM) implementation of GMLVQ and its variants is available here.