Hyperparameter optimization of svm stochastic processes
In this work we propose a SSGD based gamma optimization algorithm for Support Vector Machines model. This algorithm is combined with a nouvelle regularization technique called model dropout that make our algorithm competitive with the state of the art.
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/117926 |
| Acceso en línea: | https://hdl.handle.net/2117/117926 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Màquines de vectors de suport Regularització Optimització Support Vector Machines SVM Regularization Optimization Data Augmentation Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica |
| Sumario: | In this work we propose a SSGD based gamma optimization algorithm for Support Vector Machines model. This algorithm is combined with a nouvelle regularization technique called model dropout that make our algorithm competitive with the state of the art. |
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