Here, the neural network nodes compete with each other for the right to respond to a subset of the input data. The hidden layer is called the competitive layer. Every competitive neuron has its own weight and we calculate the similarity measure between the individual input vector and the neuron weight. For each input vector, the hidden neurons compete with each other to see which one is the most similar to the particular input vector:
The output neurons are said to be in competition for input patterns.
- During training, the output neuron that provides the highest activation to a given input pattern is declared the weights ...
