A parsimonious generation of combinatorial neural model

Autores
Prado, Hércules A.; Frigeri, Sandra; Engel, Paulo Martins
Año de publicación
1998
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents a new approach to reduce the space problem due to combinatorial explosion of CNM (Combinatorial Neural Model) method. First we show a description of CNM, proposed by Machado and Rocha [MAC 91], [MAC 92], [MAC 92a], [MAC 97], as a variation of fuzzy neural network introduced as an alternative to meet many requirements, such as expressiveness, inteligibility, plasticity and flexibility. Our approach represents an alternative to generate the CNM network with certainty factors for each hypothesis. We demonstrate by means of a simple practical example that the number of combinations can be really reduced.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
Neural nets
Learning
Data mining
data minning
knowledge discovery from databases
supervised learning
hybrid systems
neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24829
Acceder
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network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling A parsimonious generation of combinatorial neural modelPrado, Hércules A.Frigeri, SandraEngel, Paulo MartinsCiencias InformáticasInformáticaNeural netsLearningData miningdata minningknowledge discovery from databasessupervised learninghybrid systemsneural networksThis paper presents a new approach to reduce the space problem due to combinatorial explosion of CNM (Combinatorial Neural Model) method. First we show a description of CNM, proposed by Machado and Rocha [MAC 91], [MAC 92], [MAC 92a], [MAC 97], as a variation of fuzzy neural network introduced as an alternative to meet many requirements, such as expressiveness, inteligibility, plasticity and flexibility. Our approach represents an alternative to generate the CNM network with certainty factors for each hypothesis. We demonstrate by means of a simple practical example that the number of combinations can be really reduced.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)1998-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24829enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:37:32Zoai:sedici.unlp.edu.ar:10915/24829Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:37:32.999SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A parsimonious generation of combinatorial neural model
title A parsimonious generation of combinatorial neural model
spellingShingle A parsimonious generation of combinatorial neural model
Prado, Hércules A.
Ciencias Informáticas
Informática
Neural nets
Learning
Data mining
data minning
knowledge discovery from databases
supervised learning
hybrid systems
neural networks
title_short A parsimonious generation of combinatorial neural model
title_full A parsimonious generation of combinatorial neural model
title_fullStr A parsimonious generation of combinatorial neural model
title_full_unstemmed A parsimonious generation of combinatorial neural model
title_sort A parsimonious generation of combinatorial neural model
dc.creator.none.fl_str_mv Prado, Hércules A.
Frigeri, Sandra
Engel, Paulo Martins
author Prado, Hércules A.
author_facet Prado, Hércules A.
Frigeri, Sandra
Engel, Paulo Martins
author_role author
author2 Frigeri, Sandra
Engel, Paulo Martins
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
Neural nets
Learning
Data mining
data minning
knowledge discovery from databases
supervised learning
hybrid systems
neural networks
topic Ciencias Informáticas
Informática
Neural nets
Learning
Data mining
data minning
knowledge discovery from databases
supervised learning
hybrid systems
neural networks
dc.description.none.fl_txt_mv This paper presents a new approach to reduce the space problem due to combinatorial explosion of CNM (Combinatorial Neural Model) method. First we show a description of CNM, proposed by Machado and Rocha [MAC 91], [MAC 92], [MAC 92a], [MAC 97], as a variation of fuzzy neural network introduced as an alternative to meet many requirements, such as expressiveness, inteligibility, plasticity and flexibility. Our approach represents an alternative to generate the CNM network with certainty factors for each hypothesis. We demonstrate by means of a simple practical example that the number of combinations can be really reduced.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents a new approach to reduce the space problem due to combinatorial explosion of CNM (Combinatorial Neural Model) method. First we show a description of CNM, proposed by Machado and Rocha [MAC 91], [MAC 92], [MAC 92a], [MAC 97], as a variation of fuzzy neural network introduced as an alternative to meet many requirements, such as expressiveness, inteligibility, plasticity and flexibility. Our approach represents an alternative to generate the CNM network with certainty factors for each hypothesis. We demonstrate by means of a simple practical example that the number of combinations can be really reduced.
publishDate 1998
dc.date.none.fl_str_mv 1998-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24829
url http://sedici.unlp.edu.ar/handle/10915/24829
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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
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