Machine Learning for Archaeological Applications in R (Hardcover)

Denisse L. Argote

ISBN 10: 1009506595 / ISBN 13: 9781009506595

Published by Cambridge University Press, Cambridge, 2025

About this Item

Hardcover. 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. This Element highlights the employment within archaeology of classification methods developed in the field of chemometrics, artificial intelligence, and Bayesian statistics. It also provides a detailed explanation of how to process data in R as well as the respective code. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9781009506595

Bibliographic Details

• Title: Machine Learning for Archaeological Applications in R (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2025
• Language: English
• ISBN 10: 1009506595
• ISBN 13: 9781009506595
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.