| dc.contributor.advisor | Sánchez-Torres, Juan D. | |
| dc.contributor.author | Rodríguez-Reyes, Sara E. | |
| dc.date.accessioned | 2021-06-28T22:00:47Z | |
| dc.date.accessioned | 2023-03-10T16:53:22Z | |
| dc.date.available | 2021-06-28T22:00:47Z | |
| dc.date.available | 2023-03-10T16:53:22Z | |
| dc.date.issued | 2021-05 | |
| dc.identifier.citation | Rodríguez-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: ITESO | es_MX |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/68749 | |
| dc.description | This 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.language.iso | eng | es_MX |
| dc.publisher | ITESO | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf | es_MX |
| dc.subject | Extended Lagrangian | es_MX |
| dc.subject | Kernel-Based Methods | es_MX |
| dc.subject | Support Vector Regression | es_MX |
| dc.title | A Generalized Lagrange Multiplier Method Support for Vector Regression Based | es_MX |
| dc.type | info:eu-repo/semantics/masterThesis | es_MX |
