Neural Networks 17.0
Find More Complex Relationships in your Data
SPSS Neural Networks offers non-linear data modeling procedures that enable you to discover more complex relationships in your data. Using these procedures, you can develop more accurate and effective predictive models. The result? Deeper insight and better decision-making.
The procedures in SPSS Neural Networks complement the more traditional statistics in SPSS Statistics Base and its modules. Find new associations in your data with SPSS Neural Networks and then confirm their significance with traditional statistical techniques.
What is a neural network?
A computational neural network is a set of non-linear data modeling tools consisting of input and output layers plus one or two hidden layers. The connections between neurons in each layer have associated weights, which are iteratively adjusted by the training algorithm to minimize error and provide accurate predictions.
How can you use SPSS Neural Networks?
You can combine SPSS Neural Networks with other statistical procedures to gain clearer insight in a number of areas:
- Market research
- Create customer profiles
- Discover customer preferences
- Database marketing
- Segment your customer base
- Optimize campaigns
- Financial analysis
- Analyze applicants’ creditworthiness
- Detect possible fraud
- Operational analysis
- Manage cash flow
- Improve logistics planning
- Healthcare
- Forecast treatment costs
- Perform medical outcomes analysis
Use Data Mining Techniques
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.
Control the process from start to finish
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.
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.
System requirements
For SPSS Statistics Base 17.0 for Windows
Operating System
MicrosoftWindows XP (32-bit versions) or Vista(32-bit or 64-bit versions)
Hardware
Intelor AMD x86 processor running at 1GHz or higher
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 450MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
For connecting with SPSS Statistics Base Server, a network adapter running the TCP/IP network protocol
Software
Web browser: Internet Explorer 6 or above
For SPSS Statistics Base 17.0 for Mac OS X
Operating system: Apple Mac OS X 10.4 (Tiger) or Mac OSX 10.5 (Leopard)
Hardware
PowerPC or Intel processor
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 800MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Safari 1.3.1, MozillaFirefox1.5 or higher, or Netscape7.2 or higher
Java Standard Edition 5.0 (J2SE 5.0)
SPSS Statistics Base 17.0 for Linux
Operating system*
Any Linux OS that meets the following requirements:
Kernel 2.6.9.42 or higher
glibc 2.3.4 or higher
XFree86-4.0 or higher
libstdc++5
Hardware
Processor: Intel or AMD x86 processor running at 1 GHz or higher
RAM: 512MB RAM; 1GB recommended
450 MB of available hard-disk space
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Web browser: Konqueror 3.4.1 or higher, or Firefox 1.0.6 or higher, or Netscape 7.2 or higher
*Note: SPSS Statistics 17.0 was tested on and is supported only on Red HatEnterprise Linux 4 Desktop and Debian4.0
SPSS Statistics add-on modules
All SPSS Statistics 17.0 add-on modules require SPSS Statistics Base 17.0.
No other system requirements are necessary.
Amos 17.0
Operating system: Windows XP or Windows Vista
Hardware:
Memory: 256MB RAM minimum
125MB or more available hard-drive space
Web browser: Internet Explorer 6.0
SPSS Statistics Server 17.0
Operating system: Windows Server 2003 or Windows Server 2008 (32-bit or 64-bit) or Windows Server 2008 (32-bit or 64-bit); Sun Solaris (SPARC) 9 and later (64-bit only); IBMAIX5.3 and later; or Red Hat Enterprise Linux ES4 and later (64-bit); HP-UX 11i (64-bit Itanium)
Hardware
Minimum CPU: Two CPUs recommended, running at 1GHz or higher
Memory: 512MB RAM per expected concurrent user
Minimum free drive space: 300MB
Required temporary disk space: Calculate by multiplying 2.5 x number of users x expected size of dataset in megabytes
SPSS Statistics Adapter for SPSS Predictive Enterprise Services
Requires Statistics Base 17.0 and SPSS Predictive Enterprise Services
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