Identification of Mersenne prime candidates by ova-angular classification using machine learning with SVM regression and Gaussian Kernel.

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Politécnico Colombiano Jaime Isaza Cadavid
Date
2023-03-28
Abstract
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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 .
En este artículo se presentan tres números primos como altos potenciales para ser números de Mersenne y se sugiere su aplicación en testeos computacionales de primalidad. Estos números son construidos a partir de un algoritmo de regresión fundamentado en máquinas de vectores de apoyo (Support vector machine - SVM) y usando un Kernel Gaussiano. El entrenamiento de datos se lleva a cabo mediante el lenguaje de programación de Phyton, En el estudio se abordan los datos actuales de primos de Mersenne y se trabaja con el grupo de clasificación Ova-angular para primos de Mersenne. 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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Keywords
Ova-angular rotations, Mersenne’s primes, Support Vector Machine, Gaussian Kernel., Rotación Ova-Angular, Primos Mersenne, Máquinas de soporte vectorial, Kernel Gaussiano
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