A Generalized Lagrange Multiplier Method Support for Vector Regression Based
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...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | Instituto Tecnológico y de Estudios Superiores de Occidente |
| Repositorio: | Repositorio Institucional del ITESO |
| Idioma: | inglés |
| OAI Identifier: | oai:rei.iteso.mx:11117/7434 |
| Acceso en línea: | https://hdl.handle.net/11117/7434 |
| Access Level: | acceso abierto |
| Palabra clave: | Extended Lagrangian Kernel-Based Methods Support Vector Regression |
| Sumario: | 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. |
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