Automatic Generation of Neural Networks

Autores
Fiszelew, A.; Britos, Paola Verónica; Perichinsky, Gregorio; García Martínez, Ramón
Año de publicación
2003
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work deals with methods for finding optimal neural network architectures to learn par-ticular problems. A genetic algorithm is used to discover suitable domain specific architectures; this evolutionary algorithm applies direct codification and uses the error from the trained network as a per-formance measure to guide the evolution. The network training is accomplished by the back-propagation algorithm; techniques such as training repetition, early stopping and complex regulation are employed to improve the evolutionary process results. The evaluation criteria are based on learn-ing skills and classification accuracy of generated architectures.
Facultad de Informática
Materia
Ciencias Informáticas
Evolutionary computation
Neural networks
Genetic algorithms
Codification methods
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/136295
Acceder
id SEDICI_860eff6d747b431215f938ec907063bf
oai_identifier_str oai:sedici.unlp.edu.ar:10915/136295
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Automatic Generation of Neural NetworksFiszelew, A.Britos, Paola VerónicaPerichinsky, GregorioGarcía Martínez, RamónCiencias InformáticasEvolutionary computationNeural networksGenetic algorithmsCodification methodsThis work deals with methods for finding optimal neural network architectures to learn par-ticular problems. A genetic algorithm is used to discover suitable domain specific architectures; this evolutionary algorithm applies direct codification and uses the error from the trained network as a per-formance measure to guide the evolution. The network training is accomplished by the back-propagation algorithm; techniques such as training repetition, early stopping and complex regulation are employed to improve the evolutionary process results. The evaluation criteria are based on learn-ing skills and classification accuracy of generated architectures.Facultad de Informática2003info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/136295enginfo:eu-repo/semantics/altIdentifier/issn/1677-3071info:eu-repo/semantics/altIdentifier/doi/10.21529/resi.2003.0201001info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:24:10Zoai:sedici.unlp.edu.ar:10915/136295Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:24:10.974SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Automatic Generation of Neural Networks
title Automatic Generation of Neural Networks
spellingShingle Automatic Generation of Neural Networks
Fiszelew, A.
Ciencias Informáticas
Evolutionary computation
Neural networks
Genetic algorithms
Codification methods
title_short Automatic Generation of Neural Networks
title_full Automatic Generation of Neural Networks
title_fullStr Automatic Generation of Neural Networks
title_full_unstemmed Automatic Generation of Neural Networks
title_sort Automatic Generation of Neural Networks
dc.creator.none.fl_str_mv Fiszelew, A.
Britos, Paola Verónica
Perichinsky, Gregorio
García Martínez, Ramón
author Fiszelew, A.
author_facet Fiszelew, A.
Britos, Paola Verónica
Perichinsky, Gregorio
García Martínez, Ramón
author_role author
author2 Britos, Paola Verónica
Perichinsky, Gregorio
García Martínez, Ramón
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Evolutionary computation
Neural networks
Genetic algorithms
Codification methods
topic Ciencias Informáticas
Evolutionary computation
Neural networks
Genetic algorithms
Codification methods
dc.description.none.fl_txt_mv This work deals with methods for finding optimal neural network architectures to learn par-ticular problems. A genetic algorithm is used to discover suitable domain specific architectures; this evolutionary algorithm applies direct codification and uses the error from the trained network as a per-formance measure to guide the evolution. The network training is accomplished by the back-propagation algorithm; techniques such as training repetition, early stopping and complex regulation are employed to improve the evolutionary process results. The evaluation criteria are based on learn-ing skills and classification accuracy of generated architectures.
Facultad de Informática
description This work deals with methods for finding optimal neural network architectures to learn par-ticular problems. A genetic algorithm is used to discover suitable domain specific architectures; this evolutionary algorithm applies direct codification and uses the error from the trained network as a per-formance measure to guide the evolution. The network training is accomplished by the back-propagation algorithm; techniques such as training repetition, early stopping and complex regulation are employed to improve the evolutionary process results. The evaluation criteria are based on learn-ing skills and classification accuracy of generated architectures.
publishDate 2003
dc.date.none.fl_str_mv 2003
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/136295
url http://sedici.unlp.edu.ar/handle/10915/136295
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1677-3071
info:eu-repo/semantics/altIdentifier/doi/10.21529/resi.2003.0201001
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846064296230912000
score 13.22299

Publicaciones similares