Several Self-Organizing Maps were trained with a different number of
codebook vectors and varying teaching parameters. Of all these SOMs,
the one with the smallest quantization error was picked. This was the
SOM with 25 times 15 codebok vectors. Bubble neighborhood was used as
the neighborhood function. The learning rate was chosen to decrease
linearly as a function of time. Training was done in two phases.
During the first cycle, the initial alpha value 
, the
map was taught with 35000 samples and the initial neighborhood had the
value 15. The respective parameter values for the second cycle were
0.04, 150000 and 6.