After training of a neural network, the information window shows some information on the network topology, the attributes that were used, and the relative importance (weight)--on a scale from 0 to 100--of the individual attributes. Be careful to judge these weights. This information tells a lot about individual attributes, but only a little about relationships between attributes. This information can be stored into a report (a simple text file) by pressing the Save Report button. Press Dismiss to go back to the Neural Network Manager.
The information window shows neural network information for the training set and the test set. For each set the numbers of usable and invalid vectors are shown.
Balancing is automatically performed between the data extraction and the training. This ensures that there is an equal representation of all inputs picksets. Misclassification information is displayed based on the membership to the input pickset.
The information window for an unsupervised neural network will show the number of vectors assigned to each class and their average match to the corresponding class center.
The last table will display the attribute values of each class center:
When the segmentation was performed on a waveform (i.e. all input attributes contain the string "Sample") an extra option display will enable the visualization of the data of this table in a 2D viewer in order to see the class centres:
