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*.

**Training.** Specify options for the Kohonen
training algorithm used for clustering problems.

**Training
cycles.** In this box, 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:
SANN defaults to using the training
set for early stopping when no test sample is selected.

**Enable
stopping conditions.** Select the Enable
*stopping conditions* 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 will end if the average
network error improvement over a specified number of training cycles (see
next entry) is less than the Change
in e*rror
*value given here.

**Window.**
In the Window field, 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 (see above).

**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.**
Select the *Normal**
randomization* option button to use a normal randomization of weights
for the neural network model. A normal distribution (with the mean and
variance specified below) will be used to draw the initial weight values.

**Uniform randomization.**
Select the *Uniform randomization*
option button to use a uniform randomization of weights for the neural
network model. A uniform distribution (with the mean and variance specified
below) will be used to draw the initial weight values.

*Mean/Min.*
In this field, specify either the mean (for the normal distribution) or
the minimum value (for the uniform distribution) to use for drawing the
initial (i.e., before training starts) weight sample.

*Variance/Max.*
In this field, specify either the variance (for the normal distribution)
or the maximum value (for the uniform distribution) to use for drawing
the initial (i.e., before training starts) weight sample.