Description

Book 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.

Machine Learning for Archaeological Applications

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A Paperback by Denisse L. Argote

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    View other formats and editions of Machine Learning for Archaeological Applications by Denisse L. Argote

    Publisher: Cambridge University Press
    Publication Date: 9/30/2024
    ISBN13: 9781009506649, 978-1009506649
    ISBN10: 1009506641
    Also in:
    Landscape archaeology

    Description

    Book 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.

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