Find more complex relationships in your data
IBM® SPSS® Neural Networks software offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data. The software lets you set the conditions under which the network learns. You can control the training stopping rules and network architecture, or let the procedure automatically choose the architecture for you.
With IBM® SPSS® Neural Networks software, you can develop more accurate and effective predictive models.
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.
Need help leveraging the power of IBM® SPSS® software? Let us help you with our analytical expertise and experience - contact us today.