The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization.
These networks use unary coding for an effective representation of the data sets.
[1] Since then, instantaneously trained neural networks have been proposed as models of short term learning and used in web search, and financial time series prediction applications.
[4] They have also been used in instant classification of documents[5] and for deep learning and data mining.
The neurons in the hidden and output layers output 1 if the weighted sum to the input is 0 or positive and 0, if the weighted sum to the input is negative: The CC4 network has also been modified to include non-binary input with varying radii of generalization so that it effectively provides a CC1 implementation.