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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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