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dc.contributorPonce Cruz, Pedro
dc.contributorMolina Gutiérrez, Arturo
dc.contributorPedro Ponce Cruz
dc.contributorArturo Molina Gutiérrez
dc.creatorHiram Eredín Ponce Espinosa
dc.creatorHIRAM EREDÍN PONCE ESPINOSAes
dc.date.accessioned2018-05-07T19:03:58Z
dc.date.available2018-05-07T19:03:58Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/11285/628900
dc.descriptionIn this work, a new supervised learning method called artificial organic networks is proposed for modeling problems, i.e. fitting, analyzing, inference and classification. In fact, this technique is inspired on chemical organic compounds due to their characteristics of stability, encapsulation, inheritance, organization, and robustness. Additionally, this work presents artificial hydrocarbon networks, a supervised learning algorithm inspired on chemical hydrocarbon compounds and proposed under artificial organic networks technique. Theoretical and experimental results showed that both artificial organic networks technique and artificial hydrocarbon networks algorithm can be used for fitting functions, modeling systems, and inference and classification purposes. --Abstract p. ii.
dc.languageeng
dc.publisherInstituto Tecnológico y de Estudios Superiores de Monterrey
dc.relationInvestigadores
dc.relationEstudiantes
dc.relation.isformatofversión publicada
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0*
dc.subjectComputer algorithms
dc.subjectOrganic compounds
dc.subjectMachine learning
dc.subjectAlgoritmos computacionales
dc.subjectCompuestos orgánicos
dc.subjectAprendizaje automático
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleA new supervised learning algorithm inspired on chemical organic compounds
dc.typeTesis de doctorado
thesis.degree.levelDoctorado en Ciencias Computacionales
thesis.degree.disciplineEscuela de Diseño, Ingeniería y Arquitectura
thesis.degree.programCampus Ciudad de México
refterms.dateFOA2018-05-07T19:03:58Z


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