SPSS Statistics - Neural Networks

Mine your data for hidden relationships

• Choose either MLP or RBF algorithms to map relationships implied by the data. The MLP procedure can find more complex relationships, while the RBF procedure is faster.
• Benefit from feed-forward architectures, which move data in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
• Take advantage of algorithms that operate on a training set of data and then apply that knowledge to the entire data set and to any new data.

Control the process

• Specify the dependent variables, which may be scale, categorical or a combination of the two.
• Adjust each procedure by choosing how to partition the data set, which architecture to use and what computation resources to apply to the analysis.
• Choose whether to display the results in tables or graphs, save optional temporary variables to the active data set, or export models in XML-based file format to score future data.

Combine with other statistical procedures or techniques

• Confirm neural network results with traditional statistical techniques using IBM SPSS Statistics Base.
• Combine with other statistical procedures to gain clearer insight in a number of areas, including market research, database marketing, financial analysis, operational analysis and health care. In market research, for example, you can create customer profiles and discover customer preferences.