Machine Learning for Archaeological Applications in R
Denisse L. Argote, Pedro A. López‐García, Manuel A. Torres-García, Michael C. Thrun
Abstract
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.
Topics & Concepts
Computer scienceArchaeologyProcess (computing)Field (mathematics)Element (criminal law)Bayesian probabilityChemometricsArtificial intelligenceMachine learningData scienceGeographyMathematicsProgramming languageLawPolitical sciencePure mathematicsGeochemistry and Geologic MappingImage Processing and 3D ReconstructionSoil Geostatistics and Mapping