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 …
Contributors
- M. Ramazzotti (ed.)
Creator
- Candilio, F.
- Coppa, A.
- Manni, F.
Publisher
- Edizioni All'Insegna del Giglio
Subject
- Theoretical and methodological problems
- Simulation AI
- Field archaeology
- artificial intelligence
- Science and technology for Cultural Heritage
- field archaeology
- Artificial intelligence
Type of item
- articles
Date
- 2014-01-01
- 2014-01-01
Contributors
- M. Ramazzotti (ed.)
Creator
- Candilio, F.
- Coppa, A.
- Manni, F.
Publisher
- Edizioni All'Insegna del Giglio
Subject
- Theoretical and methodological problems
- Simulation AI
- Field archaeology
- artificial intelligence
- Science and technology for Cultural Heritage
- field archaeology
- Artificial intelligence
Type of item
- articles
Date
- 2014-01-01
- 2014-01-01
Providing institution
Aggregator
Intermediate provider
Rights statement for the media in this item (unless otherwise specified)
- http://creativecommons.org/licenses/by-nc-sa/4.0/
Temporal
- https://www.wikidata.org/wiki/Q6939
- 21st century
Places
- http://sws.geonames.org/3169070/
Identifier
- A_C_oai_Archive_sup.xml:810
- A_C_oai_Archive_sup.xml_810
Extent
- pp. 231-242
Language
- en
- eng
Is part of
- Europeana Archaeology
- ARCHEOSEMA. Artificial Adaptive Systems for the Analysis of Complex Phenomena. Collected Papers in Honour of David Leonard Clarke
Providing country
- Italy
Collection name
First time published on Europeana
- 2020-05-22T18:45:13.398Z
Last time updated from providing institution
- 2020-05-22T18:45:13.398Z