SFAM Neural Network
(Used For Predicting Classes)
Training begins with just one hidden node whose weights are set
equal to the first record and prediction is set equal to the
class of the first record.
Similarily, whenever a new class is encountered a new node is created.
The node whose weights best match the current input supplies
the prediction, provided the degree of match exceeds the
vigilance threshold value.
If this prediction is correct, the weights of this winning node
are adjusted toward this input.
If the prediction is wrong or vigilance threshold is not acheived,
a new node is created with weights and prediction equal to this record.