A Generalized Lagrange Multiplier Method for Support Vector Regression with Imposed Symmetry

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dc.contributor.advisorSánchez-Torres, Juan D.
dc.contributor.advisorRodríguez-Reyes, Sara E.
dc.contributor.authorGuerrero-Montaño, Luis A.
dc.date.accessioned2023-02-03T19:09:31Z
dc.date.available2023-02-03T19:09:31Z
dc.date.issued2022-11
dc.descriptionThis thesis presents an approach to support vector regression that extends the classic Vapnik’s formulation. After recalling that the classic formulation contains a Lasso regularization structure in its dual form, we propose a generalized Lagrangian function with additional terms to include the Ridge regularization in the dual problem for the case with symmetry. By including both regularization methods, the resulting dual problem with the generalized Lagrangian comprises an elastic net regularization structure. Hence, as an immediate consequence, the classical formulation is a particular case of the current proposal. Finally, to demonstrate the capabilities of this approach, the document includes examples of predicting some benchmark problems.es_MX
dc.identifier.citationGuerrero-Montaño, L. A. (2022). A Generalized Lagrange Multiplier Method for Support Vector Regression with Imposed Symmetry. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/8449
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectSVMes_MX
dc.subjectGLMMes_MX
dc.subjectSVRes_MX
dc.subjectSimetríaes_MX
dc.subjectSymmetryes_MX
dc.subjectSupport Vector Machinees_MX
dc.subjectSupport Vector Regressiones_MX
dc.titleA Generalized Lagrange Multiplier Method for Support Vector Regression with Imposed Symmetryes_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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