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SOM Learning
- The
winner and the units close to it (its neighbours) are updated by
moving their weight vectors closer to the input pattern.
- Because units
near the winner are also moved, as training progresses units that are
neighbours tend to come to represent similar patterns, while nodes far
from each other in the map represent dissimilar patterns.
- If there are
clusters of input patterns, then the points within a cluster will tend
to activate the same output unit, while points from different clusters
will be represented by separate units.
- The more dissimilar the clusters
the further apart they will be mapped in the output layer.