Abstract
Training a neural network involves a number of considerations in getting from the process to be modeled to the actual set of network training exemplars. These considerations include the following (Stein, 1993):
-
Data Specification: Deciding on what variables should be included
-
Data Collection: Collecting samples from the included variables
-
Data Inspection: Inspecting the data for characteristic and anomalous features
-
Data Conditioning: Preprocessing the data to extract features, correct for anomalies, or to reduce the volume of data
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media New York
About this chapter
Cite this chapter
Rzempoluck, E.J. (1998). Acquiring and Conditioning Network Data. In: Neural Network Data Analysis Using Simulnet™. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1746-6_4
Download citation
DOI: https://doi.org/10.1007/978-1-4612-1746-6_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7262-5
Online ISBN: 978-1-4612-1746-6
eBook Packages: Springer Book Archive