Home
Analytics
SPSS
SPSS Statistics
Neural Networks
IBM® SPSS® Neural Networks uses nonlinear data modeling to discover complex relationships and derive greater value from your data.
This module is included in the SPSS Premium edition for on-premises and in the IBM® SPSS® Forecasting and Decision Trees add-on for subscription plans.
Schedule time to discuss how SPSS Neural Networks can support your business needs.
Select multilayer perceptron (MLP) or radial basis function (RBF). Both use feedforward architectures. Data only moves from input nodes through the hidden layer of nodes to output nodes.
Display information about the neural network visually, including the dependent variables, number of input and output units, number of hidden layers and units and activation functions.
Choose to display results in tables or graphs. Save optional temporary variables to the active dataset. Export models in XML-file formats to score future data.
Specify the dependent variables, which may be scale, categorical or a combination. Adjust each procedure by choosing how to partition the dataset, which architecture to use and what computation resources to apply to the analysis.
Confirm neural network results with traditional statistical techniques. Gain clearer insight in several areas, including market research, database marketing, financial analysis, operational analysis and healthcare.