Abstract
Classification problems are, besides regression problems, another large class of machine learning problems. They belong to the category of supervised learning, and the goal is to learn how to sort data into different categories. While the ML methods are quite different from those used for regression, the fundamental aspects of learning are similar. In this chapter, we start with a detailed introduction of classification based on intuitive, rule-based classification methods. We then cover a number of the most commonly used machine learning classification methods: the class of nearest neighbor classifiers, the Gaussian naive Bayes model, and support vector machines.
“All models are wrong, but some are useful.”
George Box (191–2013)
British statistician
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Notes
- 1.
As most of the real grain microstructures result from a growth process, they obey often a lognormal distribution function. Therefore, many algorithms for creating artificial grain structures either postprocess the Voronoi structures or more recently use other algorithms.
References
F. Aurenhammer, R. Klein, and D.-T. Lee. Voronoi Diagrams and Delaunay Triangulations. World Scientific, Aug. 2013. DOI https://doi.org/10.1142/8685.
J. Lapin, K. Kamyshnykova, and A. Klimova. Comparative study of microstructure and mechanical properties of two tial-based alloys reinforced with carbide particles. Molecules, 25(15), 2020. ISSN 1420-3049. DOI https://doi.org/10.3390/molecules25153423. URL https://www.mdpi.com/1420-3049/25/15/3423.
C. Tóth, J. Goodman, and J. O’Rourke. Handbook of Discrete and Computational Geometry. Discrete mathematics and its applications. Chapman and Hall/CRC Press, 3rd edition, 2017. ISBN 9781498711395. DOI https://doi.org/10.1201/9781315119601.
I. H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, 3 edition, 2011. ISBN 978-0-12-374856-0. URL http://www.sciencedirect.com/science/book/9780123748560.
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Sandfeld, S. (2024). Supervised Classification. In: Materials Data Science. The Materials Research Society Series. Springer, Cham. https://doi.org/10.1007/978-3-031-46565-9_14
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DOI: https://doi.org/10.1007/978-3-031-46565-9_14
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