Diagnostic par Apprentissage
68 páginas
- Autores:
-
Uribe, Juan Sébastian
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2014
- Institución:
- Universidad EIA .
- Repositorio:
- Repositorio EIA .
- Idioma:
- fra
- OAI Identifier:
- oai:repository.eia.edu.co:11190/6475
- Acceso en línea:
- https://repository.eia.edu.co/handle/11190/6475
- Palabra clave:
- Apprentissage artificiel
Diagnostic automobile
Machines à vecteurs de support
Multi-classes
Régression logistique
Arbre de décision binaire
- Rights
- openAccess
- License
- Derechos Reservados - Univesidad EIA - 2014
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage Apprentissage artificiel Diagnostic automobile Machines à vecteurs de support Multi-classes Régression logistique Arbre de décision binaire |
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage |
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Diagnostic par Apprentissage |
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Uribe, Juan Sébastian |
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Uribe, Juan Sébastian |
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Apprentissage artificiel Diagnostic automobile Machines à vecteurs de support Multi-classes Régression logistique Arbre de décision binaire |
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Apprentissage artificiel Diagnostic automobile Machines à vecteurs de support Multi-classes Régression logistique Arbre de décision binaire |
| description |
68 páginas |
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2014 |
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2014 |
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2024-03-15T15:32:17Z |
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Trabajo de grado - Pregrado |
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Universidad EIA |
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Ingeniería Mecatrónica |
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Escuela de Ingeniería y Ciencias Básicas |
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Envigado, Antioquia |
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Universidad EIA |
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Uribe, Juan Sébastian2024-03-15T15:32:17Z2024-03-15T15:32:17Z2014https://repository.eia.edu.co/handle/11190/647568 páginasLe diagnostic de pannes complexes sur des véhicules automobiles est pour l’instant réalisé par des experts lorsque les outils de diagnostic automatique ne fonctionnent pas. Le but de cette étude est de poursuivre les travaux antérieurs sur la réalisation d'un apprentissage artificiel à partir d’une base de données répertoriant les pannes antérieures pour aider les experts du diagnostic, en ajoutant de nouvelles contraintes sur les sorties du classifieur. Ainsi, plusieurs résultats seront proposés avec pour chacun une probabilité de vraisemblance associée. Les outils et algorithmes qui seront développés sont basés sur les machines à vecteurs de support (SVM) et la régression logistique multi-classes.Abstract: Nowadays, fault diagnosis on complex vehicles is carried out by experts when the automatic diagnostic tools do not work. We will continue the previous work done about the feasibility study of implementing machine learning from a database of past failures to help the diagnosis experts, adding new demands made by PSA. For accomplishing this objective, we will use different classification techniques based in multi-class support vector machines (SVM) and logistic regression.PregradoIngeniero Mecatrónicoapplication/pdffraUniversidad EIAIngeniería MecatrónicaEscuela de Ingeniería y Ciencias BásicasEnvigado, AntioquiaDerechos Reservados - Univesidad EIA - 2014https://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Diagnostic par ApprentissageTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/TPhttp://purl.org/coar/version/c_970fb48d4fbd8a85Apprentissage artificielDiagnostic automobileMachines à vecteurs de supportMulti-classesRégression logistiqueArbre de décision binairePublicationORIGINALUribeJuan_2014_DiagnosticParApprentisage.pdfUribeJuan_2014_DiagnosticParApprentisage.pdfapplication/pdf2676495https://repository.eia.edu.co/bitstreams/4b392dc9-a3bd-46b4-97e1-7f5f24e30eb6/download28c82704d49f605f575ba1d52d90367eMD51LICENSElicense.txtlicense.txttext/plain; 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