Please use this identifier to cite or link to this item: 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
Appears in Collections:Doctorado en Ciencias Computacionales

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