Titlebook: Computational and Machine Learning Tools for Archaeological Site Modeling; Maria Elena Castiello Book 2022 The Editor(s) (if applicable) a

骇人

Results and Discussionion. An analysis of the partial dependence indicates the effects of the variables on the predicted outcome of the Machine Learning model. Finally, the validity assessment procedure, ad hoc created for this model, highlights the limitations and the advantages of the Random Forest-based approach for Archaeological Site Modeling.

parsimony

Conclusionsn of site detection and the issue of preservation and conservation of archaeological sites in the long-term perspective by combining cutting-edge technologies with analytical archaeological reasoning. It represents a unique and innovative approach for modeling archaeological sites at any spatio-temporal scale.

壕沟

https://doi.org/10.1007/978-3-662-10179-7ness inherent to the data and its delivering in Archaeological Predictive Maps is further tackled and summarized. Finally, more complex, non-linear machine learning and data mining approaches are described, with particular emphasis on the Random Forest algorithm as fundamental part of the methodological procedure developed in this study.

哥哥喷涌而出

,Verzeichnis der verwendeten Abkürzungen,omparative way, creating a common data architecture allowing for supra-regional analyses. Furthermore, the geo-environmental variables assumed to have influenced site location choices during Roman times and used as predictors in the modeling procedure are described.

Gourmet

Symptoms and Signs in Pediatric Surgeryion. An analysis of the partial dependence indicates the effects of the variables on the predicted outcome of the Machine Learning model. Finally, the validity assessment procedure, ad hoc created for this model, highlights the limitations and the advantages of the Random Forest-based approach for Archaeological Site Modeling.

PAC

不透明性

Book 2022nd suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.. . .

Intrepid

Computational and Machine Learning Tools for Archaeological Site Modeling

jaunty

脆弱带来

Maria Elena CastielloNominated as an outstanding PhD thesis by the University of Bern, Switzerland.Describes novel methods for investigating archaeological settlement patterns and locational preference choices.Proposes a