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 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.
Product details
No date availableAdobe eBook Reader
9781009506618
0 pages
Table of Contents
- 1. Introduction
- 2. Processing spectral data
- 3. Processing compositional data
- 4. Processing a combination of spectral and compositional data
- 5. Final comments
- Abbreviations
- References.