Machine learning for archaeological applications in R:

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...

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Bibliographische Detailangaben
1. Verfasser: Argote, Denisse L. (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Cambridge Cambridge University Press 2024
Schriftenreihe:Cambridge elements
Schlagworte:
Online-Zugang:DE-12
DE-473
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Zusammenfassung: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
Beschreibung:Title from publisher's bibliographic system (viewed on 18 Dec 2024)
Beschreibung:1 Online-Ressource (90 Seiten)
ISBN:9781009506625
DOI:10.1017/9781009506625

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