A Generalized Lagrange Multiplier Method for Support Vector Regression with Imposed Symmetry
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Fecha
2022-11
Autores
Guerrero-Montaño, Luis A.
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ITESO
Resumen
Descripción
This 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.
Palabras clave
SVM, GLMM, SVR, Simetría, Symmetry, Support Vector Machine, Support Vector Regression
Citación
Guerrero-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.