Machine Learning for Archaeological Applications in R

This Element highlights the employment within archaeology of classification methods in chemometrics, AI, and Bayesian statistics.

Denisse L. Argote (Author), Pedro A. López-­García (Author), Manuel A. Torres-­García (Author), Michael C. Thrun (Author)

9781009506595, Cambridge University Press

Hardback, published 16 January 2025

96 pages
22.9 x 15.2 x 0.6 cm, 0.281 kg

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.

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

Subject Areas: Archaeology [HD]