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dc.contributor.authorDe Butléir, Amy
dc.contributor.authorDaly, Mark
dc.contributor.authorRussell, Mike
dc.identifier.citationde Buitléir A., Daly M., Russell M. (2018) The Self-Generating Model: An Adaptation of the Self-organizing Map for Intelligent Agents and Data Mining. In: Lewis P., Headleand C., Battle S., Ritsos P. (eds) Artificial Life and Intelligent Agents. ALIA 2016. Communications in Computer and Information Science, vol 732. Springer, Cham.
dc.identifier.otherOther - Electronics, Computer & Software Engineering AITen_US
dc.description.abstractWe present the Self-Generating Model (SGM), a new version of the Self-organizing Map (SOM) that has been adapted for use in intelligent data mining ALife agents. The SGM sacrifices the topology-preserving ability of the SOM, but is equally accurate, and faster, at identifying handwritten numerals. It achieves a higher accuracy faster than the SOM. Furthermore, it increases model stability and reduces the problem of “wasted” models. We feel that the SGM could be a useful alternative to the SOM when topology preservation is not required.en_US
dc.publisherSpringer Internationalen_US
dc.relation.ispartofArtificial Life and Intelligent Agentsen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.subjectSelf-organizing mapen_US
dc.subjectArtificial lifeen_US
dc.subjectIntelligent agentsen_US
dc.titleThe self-generating model: an adaptation of the self-organizing map for intellgent agents and data mining.en_US
dc.typeBook chapteren_US
dc.rights.accessOpen Accessen_US
dc.subject.departmentFaculty of Engineering & Informatics AITen_US

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland