Identificación de candidatos a primos Mersenne mediante clasificación ova-angular utilizando aprendizaje automático con regresión SVM y Kernel Gaussiano

Yeisson Alexis Acevedo-Agudelo; Gabriel Ignacio Loaiza-Ossa

doi:10.33571/rpolitec.v19n37a7

Authors

Yeisson Alexis Acevedo-Agudelo Universidad EAFIT https://orcid.org/0000-0002-1640-9084
Gabriel Ignacio Loaiza-Ossa Universidad EAFIT https://orcid.org/0000-0003-2413-1139

DOI:

https://doi.org/10.33571/rpolitec.v19n37a7

Keywords:

Ova-angular rotations, Mersenne’s primes, Support Vector Machine, Gaussian Kernel.

Abstract

In this paper three prime numbers are presented as high potentials to be Mersenne numbers and their application in computational primality testing is suggested. These numbers are constructed from a regression algorithm based on Support vector machines (SVM) and using a Gaussian Kernel. Data training is carried out using the Phyton programming language, In the study we address the current data of Mersenne primes and work with the Ova-angular classification group for Mersenne primes .

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Author Biographies

Yeisson Alexis Acevedo-Agudelo, Universidad EAFIT

Magister en Matemáticas Aplicadas

Gabriel Ignacio Loaiza-Ossa, Universidad EAFIT

Doctor en Ciencias Matemáticas

References

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