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.
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de 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. https://doi.org/10.1007/978-3-319-90418-4_5
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