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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/24829
Ver los metadatos del registro completo
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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:UNLP2025-09-29T10:56:03Zoai: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:13292025-09-29 10:56:03.849SEDICI (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 |
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eng |
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eng |
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openAccess |
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