SPSS Neural Networks provides a complementary approach to the data analysis techniques available in SPSS Statistics Base and its modules. From the familiar SPSS Statistics interface, you can “mine” your data for hidden relationships, using either the Multilayer Perceptron (MLP) or Radial Basis Function (RBF) procedure.
Both of these are supervised learning techniques—that is, they map relationships implied by the data. Both use feed-forward architectures, meaning that data moves in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
Your choice of procedure will be influenced by the type of data you have and the level of complexity you seek to uncover. While the MLP procedure can find more complex relationships, the RBF procedure is generally faster.
With either of these approaches, the procedure operates on a training set of data and then applies that knowledge to the entire dataset, and to any new data.
After selecting a procedure, you specify the dependent variables, which may be scale, categorical, or a combination of the two. You adjust the procedure by choosing how to partition the dataset, what sort of architecture you want, and what computation resources will be applied to the analysis.
Just as you do when using SPSS Base or other modules, from the dialog boxes in SPSS Neural Networks, you select the variables that you want to include in your model.
Finally, you choose whether you want to display results in tables or graphs, save optional temporary variables to the active dataset, and/or export models in XML-based file format to score future data.
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