| dc.creator | ALEJANDRO ROSALES PEREZ | |
| dc.date | 2016-01-15 | |
| dc.date.accessioned | 2023-07-25T16:20:45Z | |
| dc.date.available | 2023-07-25T16:20:45Z | |
| dc.identifier | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/36 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7805259 | |
| dc.description | 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. | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| dc.relation | citation:Rosales-Perez A. | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.subject | info:eu-repo/classification/Toma de decisiones/Decision marking | |
| dc.subject | info:eu-repo/classification/Estrategia de evaluación/Evolution strategy | |
| dc.subject | info:eu-repo/classification/Algoritmos genéticos/Genetic algorithms | |
| dc.subject | info:eu-repo/classification/Mecanismos de validación/Mechasnisms for validation | |
| dc.subject | info:eu-repo/classification/cti/1 | |
| dc.subject | info:eu-repo/classification/cti/12 | |
| dc.subject | info:eu-repo/classification/cti/1203 | |
| dc.subject | info:eu-repo/classification/cti/120307 | |
| dc.subject | info:eu-repo/classification/cti/120307 | |
| dc.title | Surrogate-assisted evolutionary multi-objective full model selection | |
| dc.type | info:eu-repo/semantics/doctoralThesis | |
| dc.audience | generalPublic | |
