Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
LCP
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"
Image for the paper "Machine learning class numbers of real quadratic fields"

AI for real quadratic fields

Supervised learning experiments involving real quadratic fields lead to machine-learned formulas for class numbers 1, 2 and 3, for our dataset.

Machine learning class numbers of real quadratic fields

International Journal of Data Science in the Mathematical Sciences (2023)

M. Amir, Y. He, K. Lee, T. Oliver, E. Sultanow

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.

International Journal of Data Science in the Mathematical Sciences (2023)

M. Amir, Y. He, K. Lee, T. Oliver, E. Sultanow