Preprint: Machine-learning number fields : WestminsterResearch

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Title	Preprint: Machine-learning number fields
Authors	He, Y.-H., Lee, K.-H. and Oliver, T.
Description	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 0.96 precision. 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 2, 6, and 8, a logistic regression classifier can distinguish between Galois groups and predict the ranks of unit groups with precision >0.97.
Year	2020
Output media	arXiv preprint
Publisher	arXiv
Published	17 Nov 2020
ISSN	2331-8422
Digital Object Identifier (DOI)	https://doi.org/10.48550/arxiv.2011.08958
Journal	ARXIV

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Preprint: Machine-learning number fields

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