A Generalized Lagrange Multiplier Method Support for Vector Regression Based

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dc.contributor.advisorSánchez-Torres, Juan D.
dc.contributor.authorRodríguez-Reyes, Sara E.
dc.date.accessioned2021-06-28T22:00:47Z
dc.date.available2021-06-28T22:00:47Z
dc.date.issued2021-05
dc.descriptionThis research presents an approach to support vector regression based on the epsilon L1 and L2 formulations. In contrast to standard architectures, it explores a new formulation where the dual optimization problem results from formulating an extended Lagrangian function, introducing additional terms to include a weighted elastic net regularization structure. Additionally, the research shows the differences and similarities of this proposal with the classical support vector regression and the LASSO regression, aiming to compare them with standard models. To demonstrate the capabilities of this approach, the document includes examples of predicting some benchmark functions.es_MX
dc.identifier.citationRodríguez-Reyes, Sara E. (2021). A Generalized Lagrange Multiplier Method Support for Vector Regression Based. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESOes_MX
dc.identifier.urihttps://hdl.handle.net/11117/7434
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectExtended Lagrangianes_MX
dc.subjectKernel-Based Methodses_MX
dc.subjectSupport Vector Regressiones_MX
dc.titleA Generalized Lagrange Multiplier Method Support for Vector Regression Basedes_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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