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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.
