Machine Learning for Archaeological Applications in R

By: Denisse L. Argote, Pedro A. LÃ³pez-ÂGarcÃa, Manuel A. Torres-ÂGarcÃa, Michael C. Thrun

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Hardcover | 16 January 2025

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Hardcover
98 Pages

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15.9 x 23.6 x 1.4

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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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