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A supervised machine-learning approach towards geochemical predictive modelling in archaeology

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References

1996Identification of Activity Areas by Multi-element Characterization of Sediments from Modern and Archaeological House Floors Using Inductively Coupled Plasma-atomic Emission Spectroscopy246 cited
1994Chemical Characterization of Various Archaeological Soil Samples using Main and Trace Elements determined by Inductively Coupled Plasma Atomic Emission Spectrometry86 cited
2005Soil geochemistry and detection of early Roman precious metal and copper alloy working at the Roman town of Calleva Atrebatum (Silchester, Hampshire, UK)52 cited

Cited by

2023New technologies for the conservation and preservation of cultural heritage through a bibliometric analysis26 cited
2022Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments20 cited
2022Formation of reference groups for archaeological pottery using neutron activation and multivariate statistical analyses18 cited
2020What settlements leave behind — pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning13 cited
2019Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry13 cited

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