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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/36| Surrogate-assisted evolutionary multi-objective full model selection | |
| ALEJANDRO ROSALES PEREZ | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Decision marking Evolution strategy Genetic algorithms Mechasnisms for validation | |
| Classification problems have become a popular task in pattern recognition. This is, perhaps, because they can be used in a number of problems, such as text categorization, handwriting recognition, etc. This has resulted in a large number of methods. Some of theses methods, called pre-processing, aim at preparing the data to be used and others, called learning algorithms, aim at learning a model that maps from the input data into a category. Additionally, most of them have a set of adjustable parameters, called hyper-parameters, that directly impact the performance of the learned models. Hence, when a classification model is constructed, one has to choose among the set of methods and to configure the corresponding hyper-parameters, which can result in a decision with a high number of degrees of freedom. The latter could be a shortcoming when non-expert machine learning users have to face such a problem. | |
| Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| 15-01-2016 | |
| Tesis de doctorado | |
| Inglés | |
| Público en general | |
| Rosales-Perez A. | |
| MODELOS CAUSALES | |
| Aparece en las colecciones: | Doctorado en Ciencias Computacionales |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| RosalesPeA.pdf | 864.97 kB | Adobe PDF | Visualizar/Abrir |