A Generalized Lagrange Multiplier Method for Support Vector Regression with Imposed Symmetry
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 Ridg...
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| Tipo de documento: | dissertação |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2022 |
| País: | México |
| Recursos: | Instituto Tecnológico y de Estudios Superiores de Occidente |
| Repositório: | Repositorio Institucional del ITESO |
| Idioma: | inglês |
| OAI Identifier: | oai:rei.iteso.mx:11117/8449 |
| Acesso em linha: | https://hdl.handle.net/11117/8449 |
| Access Level: | Acceso aberto |
| Palavra-chave: | SVM GLMM SVR Simetría Symmetry Support Vector Machine Support Vector Regression |
| Resumo: | 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. |
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