The general case of using patterns with a neural network is an exact
fit of the patterns onto the network. The set of activations of all
input units is called input pattern, the set of activations of all
output units is called output pattern. The input pattern and its
corresponding output pattern is simply called a pattern. This definition
implies that all patterns for a particular network have the same
size. These patterns will be called * regular* or * fixed
sized*.

SNNS also offers another, much more flexible type of pattern. These
patterns will be called * variable sized*. Here, the patterns are
usually larger than the input/output layers of the network. To train
and recall these patterns small portions (subsequently called *
subpatterns*) are systematically cut out from the large pattern and
propagated through the net, one at a time. Only the smaller
subpatterns do have to have the fixed size fitting the network. The
pattern itself may have an arbitrary size and different patterns
within one pattern set may have differing sizes. The number of
variable dimensions is also variable. Example applications for one and
two variable dimensions include time series patterns for TDNNs and
picture patterns.

Both of these types of patterns are loaded into SNNS from the same kind of pattern file. For a detailed description of the structure of this file see sections and . The grammar is given in appendix

Niels.Mache@informatik.uni-stuttgart.de

Tue Nov 28 10:30:44 MET 1995