English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/144179
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Discovering Prehistoric Ritual Norms. A Machine Learning Approach

AuthorsDuboscq, Stephanie; Barceló-Álvarez, Juan Antonio; Achino, Katia; Morell, Berta; Alliése, F.; Gibaja, Juan Francisco
KeywordsSepulcres de Fossa
Statistics and Supervised Learning
Ritual Patterns
Funerary practices
Issue Date2016
Università di Siena
CitationCAA2015. Proceeding of the 43rd Annual Conference on Computer Applications and Quantitative Methods in Archaeology : 837-844 (2016)
AbstractIn this paper we propose a computational approach, based on the application of supervised learning techniques, in order to understand prehistoric funerary practices. In particular we focus on the understanding of relevant ritual pattems from North-Eastem Iberian Peninsula Middle Neolithic burials. We compare standard statistical multidimensional approaches with machine learning methods based on a supervised learning approach in which the relevant category to be formally induced is the sex of the individuals. Different analysis will be explored, as Cluster and Correspondence analysis and Decision Trees to show how we can define social norms in the archaeological record based on detecting relevant differences between controlled categories. Of special relevance for our purposes is the comparison between 'classical ' Confirmatory Factor Analysis ofburial similarities and the machine learning approach to conceptual induction.
Publisher version (URL)http://archaeopress.com/ArchaeopressShop/Public/download.asp?id={77DEDD4E-DE8F-43A4-B115-ABE0BB038DA7}
Identifiersisbn: 9781784913389
Appears in Collections:(IMF) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.