Machine Learning Class Numbers of Real Quadratic Fields
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866912183504338944 |
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| author | Amir, Malik He, Yang-Hui Lee, Kyu-Hwan Oliver, Thomas Sultanow, Eldar |
| author_facet | Amir, Malik He, Yang-Hui Lee, Kyu-Hwan Oliver, Thomas Sultanow, Eldar |
| contents | We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3. We quantify the relative difficulties in separating class numbers of matching/different parity from a data-scientific perspective, apply the methodology of feature analysis and principal component analysis, and use symbolic classification to develop machine-learned formulas for class numbers 1, 2 and 3 that apply to our dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_09283 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Machine Learning Class Numbers of Real Quadratic Fields Amir, Malik He, Yang-Hui Lee, Kyu-Hwan Oliver, Thomas Sultanow, Eldar Number Theory Machine Learning High Energy Physics - Theory 11R29, 11R80, 62R99 We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3. We quantify the relative difficulties in separating class numbers of matching/different parity from a data-scientific perspective, apply the methodology of feature analysis and principal component analysis, and use symbolic classification to develop machine-learned formulas for class numbers 1, 2 and 3 that apply to our dataset. |
| title | Machine Learning Class Numbers of Real Quadratic Fields |
| topic | Number Theory Machine Learning High Energy Physics - Theory 11R29, 11R80, 62R99 |
| url | https://arxiv.org/abs/2209.09283 |