The self-generating model: An adaptation of the self-organizing map for intelligent agents and data mining

Amy de Buitléir, Mark Daly, Michael Russell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We 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.

Original languageEnglish
Title of host publicationArtificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers
EditorsChristopher J. Headleand, Panagiotis D. Ritsos, Steve Battle, Peter R. Lewis
PublisherSpringer-Verlag GmbH and Co. KG
Pages59-72
Number of pages14
ISBN (Print)9783319904177
DOIs
Publication statusPublished - 2018
Event2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016 - Birmingham, United Kingdom
Duration: 14 Jun 201615 Jun 2016

Publication series

NameCommunications in Computer and Information Science
Volume732
ISSN (Print)1865-0929

Conference

Conference2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016
Country/TerritoryUnited Kingdom
CityBirmingham
Period14/06/1615/06/16

Keywords

  • Artificial life
  • Intelligent agents
  • Self-organizing map

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