Un Supervised Learning. & Self Organizing Maps. Learning From Examples. 1 9 16 36 25 4 . 1 3 4 6 5 2. Supervised Learning.
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Self Organizing Maps
we say that the learning is Supervised
until a final configurationdevelops
The change in weight depends on the product of the neuron’s
output and input, with a term that makes the weights decrease
Alternatively, we could have normalized the weight vector after
each update, keeping its norm to be one.
Linsker (1986) proposed a model of self organization in the visual system, based on unsupervised Hebbian learning