Competitive Learning Rule | Artificial Neural Networks
Also referred to as Winner-takes all learning rule, it is an unsupervised algorithm learning rule.
This rule uses the competitive network to represent the input patterns in unsupervised training. Output neurons in this competitive network compete to win during training. The output neuron with the highest activation to a given input pattern is automatically the winner in the ‘competition’, and is therefore selected to represent the input patters. The winning neuron is the one whose weight is adjusted accordingly in the learning process to specialize it.
The competitive learning algorithm rule is used to find clusters within data.
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Competitive Learning Rule | ANN Learning Rules
Artificial Neural Networks | thetqweb