  Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern
Recognition tasks. Their major advantage over other architectures is human
readability of a model. However, they often gain poorer accuracy. Mostly used
metric in SOM is the Euclidean distance, which is not the best approach to some
problems. In this paper, we study an impact of the metric change on the SOM's
performance in classification problems. In order to change the metric of the
SOM we applied a distance metric learning method, so-called 'Large Margin
Nearest Neighbour'. It computes the Mahalanobis matrix, which assures small
distance between nearest neighbour points from the same class and separation of
points belonging to different classes by large margin. Results are presented on
several real data sets, containing for example recognition of written digits,
spoken letters or faces.
