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Kohonen self-organizing Maps to unravel patterns of dental morphology in space and time

The paper illustrates how the application of a specific version of Artificial Neural Networks, Self-Organizing Maps (SOMs), enabled a more accurate analysis of human dental morphology. SOMs enable the processing of individual samples (dentitions) because they can cope with missing data. In fact, in archaeological samples of human remains, teeth are often broken or missing making a complete set of …

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Contribuitori

  • M. Ramazzotti (ed.)

Creator

  • Candilio, F.
  • Coppa, A.
  • Manni, F.

Editor

  • Edizioni All'Insegna del Giglio

Subiect

  • Theoretical and methodological problems
  • Simulation AI
  • Field archaeology
  • artificial intelligence
  • Science and technology for Cultural Heritage
  • field archaeology
  • Inteligență artificială

Tipul resursă culturală

  • articles

Dată

  • 2014-01-01
  • 2014-01-01