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

Simple item page
dc.creatorAcevedo-Agudelo, Yeisson Alexis
dc.creatorLoaiza-Ossa, Gabriel Ignacio
dc.date2023-03-28
dc.date.accessioned2023-06-29T13:34:22Z
dc.date.available2023-06-29T13:34:22Z
dc.descriptionIn 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-US
dc.descriptionEn 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 .es-ES
dc.formatapplication/pdf
dc.formattext/html
dc.identifierhttps://revistas.elpoli.edu.co/index.php/pol/article/view/2132
dc.identifier10.33571/rpolitec.v19n37a7
dc.identifier.urihttps://repositorio.elpoli.edu.co/handle/123456789/1135
dc.languagespa
dc.publisherPolitécnico Colombiano Jaime Isaza Cadavides-ES
dc.relationhttps://revistas.elpoli.edu.co/index.php/pol/article/view/2132/2147
dc.relationhttps://revistas.elpoli.edu.co/index.php/pol/article/view/2132/2084
dc.relationhttps://revistas.elpoli.edu.co/index.php/pol/article/view/2132/2102
dc.rightsDerechos de autor 2023 Yeisson Alexis Acevedo-Agudelo, Gabriel Ignacio Loaiza-Ossaes-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceRevista Politécnica; Vol. 19 No. 37 (2023): January-June, 2023; 103-110en-US
dc.sourceRevista Politécnica; Vol. 19 Núm. 37 (2023): Enero-Junio, 2023; 103-110es-ES
dc.sourceRevista Politécnica; v. 19 n. 37 (2023): Janeiro-Junho, 2023; 103-110pt-BR
dc.source2256-5353
dc.source1900-2351
dc.subjectOva-angular rotationsen-US
dc.subjectMersenne’s primesen-US
dc.subjectSupport Vector Machineen-US
dc.subjectGaussian Kernel.en-US
dc.subjectRotación Ova-Angulares-ES
dc.subjectPrimos Mersennees-ES
dc.subjectMáquinas de soporte vectoriales-ES
dc.subjectKernel Gaussianoes-ES
dc.titleIdentification of Mersenne prime candidates by ova-angular classification using machine learning with SVM regression and Gaussian Kernel.en-US
dc.titleIdentificación de candidatos a primos Mersenne mediante clasificación ova-angular utilizando aprendizaje automático con regresión SVM y Kernel Gaussianoes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
Files
Collections
Revista Politécnica