SANN - Custom Neural Network - Kohonen Training Tab

You can select the Kohonen Training tab of the SANN - Custom Neural Network dialog box to access the options described here. This tab is only available for cluster analysis types. For information on the options that are common to all tabs (located at the top and on the lower-right side of the dialog box), see SANN - Custom Neural Network.

Option Description
Training Specifies options for the Kohonen training algorithm used for clustering problems.
Training cycles You can enter the number of training cycles to use.
Learning rates The Kohonen learning rate is altered linearly from the first to last training cycle. You can specify a Start and End value.
Neighborhoods This is the radius of a square neighborhood centered on the winning unit. For example, a neighborhood size of 2 specifies a 5x5 square.

If the winning node is placed near or on the edge of the topological map, the neighborhood is clipped to the edge.

The neighborhood is scaled linearly from the Start value to the End value given.

Stopping conditions Use the options in this group box to specify when to apply the stopping conditions for early stopping of network training.
Note: The default behavior is using the training set for early stopping when no test sample is selected.
Enable stopping conditions Select the check box to implement early stopping to the training of the neural network. Early stopping is applied when the conditions defined below are met by the training algorithm.
Change in error When stopping conditions are applied, network training ends if the average network error improvement over a specified number of training cycles is less than the Change in error value given here.
Window Enter the number of training cycles over which the average improvement in network error must be at least as large as the specified Change in error.
Network randomization Use the options in this group to specify how the weights should be initialized at the beginning of training. In addition to selecting a distribution, also specify the mean/min and variance/max to use.
Normal randomization Uses a normal randomization of weights for the neural network model. A normal distribution (with the mean and variance specified) is used to draw the initial weight values.
Uniform randomization Uses a uniform randomization of weights for the neural network model. A uniform distribution (with the mean and variance specified) is used to draw the initial weight values.
Mean/Min Specifies either the mean (for the normal distribution) or the minimum value (for the uniform distribution) to use for drawing the initial (that is, before training starts) weight sample.
Variance/Max Specifies either the variance (for the normal distribution) or the maximum value (for the uniform distribution) to use for drawing the initial (that is, before training starts) weight sample.