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SPSS Statistics 24 - Neural Networks versions & prices
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IBM SPSS Neural Networks offers non-linear data modeling procedures that enable you to discover more complex relationships in your data.

Choose from algorithms that can be used for classification (categorical outcomes) and prediction (numerical outcomes) to develop more accurate and effective predictive models that provide deeper insight and better decision-making.

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IBM SPSS Statistics - Neural Networks

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 SPSS Neural Networks software, you can develop more accurate and effective predictive models.

  • Mine your data for hidden relationships using the Multilayer Perceptron (MLP) or Radial Basis Function (RBF) procedure.
  • Control the process from start to finish by specifying the variables.
  • Combine with other statistical procedures or techniques for greater insight.

Desktop-Systems

  Windows® Mac® OS X Linux®
Further Requirements Super VGA-Monitor (800x600) or higher Resolution
For a connection to SPSS Statistics Base Server, you will need a network adapter for TCP/IP-Network protocol
Internet Explorer
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Operating System Windows XP, Vista, 7, 8, 10 (32-/64-Bit) Mac OS X 10.7 (32-/64-Bit), Mac OS X 10.8 (only 64-Bit!) Debian 6.0 x86-64, Red Hat Enterprise Linux (RHEL) 5 Desktop Editions, Red Hat Enterprise Linux (RHEL) Client 6 x86-64:
  • Linux (64 bit) kernel 2.6.28-238.e15 or higher
  • FORTRAN version libgfortran.so.3
  • C++ Version libstdc++.so.6.0.10
Min. CPU Intel or AMD-x86-Processor 1 GHz or better Intel-Processor (32-/64-Bit) Intel or AMD-x86-Processor 1 GHz or better  
Min. RAM 1 GB RAM + 1 GB RAM + 1 GB RAM +
Festplattenplatz Min. 800 MB Min. 800 MB Min. 800 MB

Server-Systems

  SPSS Statistics Server
Further Requirements For Windows-, Solaris-PC's: Network adapter with TCP/IP-Network protocol
For System z-PC's: OSA-Express3 10 Gigabit Ethernet, OSA-Express3 Gigabit Ethernet, OSA-Express3 1000BASE-T Ethernet
Operating System Windows Server 2008 or 2012 (64-Bit), Red Hat Enterprise Linux 5 (32-/64-Bit), SUSE Linux Enterprise Server 10 and 11 (32-/64-Bit)

Details can be found in the the following PDF-document:System Requirements SPSS Statistics Server 22
Min. CPU  
Min. RAM 4 GB RAM +
Disk Space ca. 1 GB for the installation. Double the amount may be needed.

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.