This information needs to be augmented by statistical analysis of the input data. We will not elaborate on data analysis techniques, however it is important to remember that this analysis helps significantly in our main goal, which is network analysis. During the learning the network acquires the structure of the data from the exemplars. By comparing what we see in the data and what the network learned, we can better judge the network. The network function is a very complex and highly nonlinear function defined over a region of high dimensionality.
Any single method provides only a very limited insight into the function.
It takes a combination of the methods presented in this section with the algebraic and rule extraction methods described in the two following chapters to get satisfactory picture. The network function is obtained as a result of training, which is forcing it to be equal to 1 for one class of exemplars and to 0 for the other class. Thus the function takes on one of the two values 1 or 0 over most of its domain. The rest of the domain is providing space for output transition from 1 to 0, in it lie the boundaries separating the two classes. The behavior of the function in the vicinity of exemplars and in the boundary region tells us a lot about the generalization properties of the network.