Introduction to NeuNet Pro |
Introduction What is Neural Net? Suggested Uses SFAM Classification Back Propagation NeuNet Overview |
SFAM ClassificationClassification is a type of problem where one attempts to predict the correct class or category for a given pattern where two or more classes are possible. NeuNet Pro will allow up to 256 possible classes in an SFAM project.
Many problems involve a choice between only two possible classes.
Other problems can involve dozens or hundreds of classes.
The SFAM Algorithm
SFAM Strengths:
SFAM Weaknesses:
A Typical 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. Similarly, 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 achieved, a new node is created with weights and prediction equal to this record. |
A Complete Neural Network Development System |