Machine-learning number fields : WestminsterResearch

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Title	Machine-learning number fields
Type	Journal article
Authors	He, Y.-H., Lee, K.-H. and Oliver, T.
Abstract	We show that standard machine-learning algorithms may be trained to predict certain invariants of algebraic number fields to high accuracy. A random-forest classifier that is trained on finitely many Dedekind zeta coefficients is able to distinguish between real quadratic fields with class number 1 and 2, to precision 0.96. Furthermore, the classifier is able to extrapolate to fields with discriminant outside the range of the training data. When trained on the coefficients of defining polynomials for Galois extensions of degrees , , and , a logistic regression classifier can distinguish between Galois groups and predict the ranks of unit groups with precision >0.97.
Journal	Mathematics, Computation and Geometry of Data
Journal citation	2 (1), pp. 49-66
ISSN	2642-1909
	2642-1917
Year	2022
Publisher	International Press
Digital Object Identifier (DOI)	https://doi.org/10.4310/MCGD.2022.v2.n1.a2
Web address (URL)	https://www.intlpress.com/site/pub/pages/journals/items/mcgd/content/vols/0002/0001/a002/index.php?mode=ns
Published	21 Oct 2022

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Machine-learning number fields

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