Machine Learning for Archaeological Applications in R

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Denisse L. Argote (Instituto Nacional de Antropología e Historia) Pedro A. López-García (Escuela Nacional de Antropología e Historia)

Machine Learning for Archaeological Applications in R

Denisse L. Argote (Instituto Nacional de Antropología e Historia), Pedro A. López-García (Escuela Nacional de Antropología e Historia), Manuel A. Torres-García (Instituto Nacional de Antropología e Historia) , Michael C. Thrun (Philipps-Universität Marburg, Germany)

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English

Cambridge University Press
16 January 2025

Landscape archaeology; Pattern recognition

Series: Elements in Current Archaeological Tools and Techniques

This Element highlights the employment within archaeology of classification methods developed in the field of chemometrics, artificial intelligence, and Bayesian statistics. These run in both high- and low-dimensional environments and often have better results than traditional methods. Instead of a theoretical approach, it provides examples of how to apply these methods to real data using lithic and ceramic archaeological materials as case studies. A detailed explanation of how to process data in R (The R Project for Statistical Computing), as well as the respective code, are also provided in this Element.

By: Denisse L. Argote (Instituto Nacional de Antropología e Historia), Pedro A. López-García (Escuela Nacional de Antropología e Historia), Manuel A. Torres-García (Instituto Nacional de Antropología e Historia), Michael C. Thrun (Philipps-Universität Marburg, Germany)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
ISBN: 9781009506649
ISBN 10: 1009506641
Series: Elements in Current Archaeological Tools and Techniques
Pages: 96
Publication Date: 16 January 2025
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

1. Introduction; 2. Processing spectral data; 3. Processing compositional data; 4. Processing a combination of spectral and compositional data; 5. Final comments; Abbreviations; References.