A novel art for binary pattern recognition

Autores
Matsunaga, G.; Rey, H. G.; Zanutto, S.; Cernuschi Frías, Bruno
Año de publicación
2002
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequence of input patterns. In this work we explore its drawbacks and propose several modi cations. Comparativesimulations show the better performance of our algorithm.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Adaptive Resonance Theory
Binary Pattern Recognition
Pattern Classification
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:10915/183334
Acceder
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spelling A novel art for binary pattern recognitionMatsunaga, G.Rey, H. G.Zanutto, S.Cernuschi Frías, BrunoCiencias InformáticasAdaptive Resonance TheoryBinary Pattern RecognitionPattern ClassificationAdaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequence of input patterns. In this work we explore its drawbacks and propose several modi cations. Comparativesimulations show the better performance of our algorithm.Sociedad Argentina de Informática e Investigación Operativa2002-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf23-34http://sedici.unlp.edu.ar/handle/10915/183334spainfo:eu-repo/semantics/altIdentifier/issn/1666-1095info: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:UNLP2025-09-03T11:21:45Zoai:sedici.unlp.edu.ar:10915/183334Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:21:45.897SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A novel art for binary pattern recognition
title A novel art for binary pattern recognition
spellingShingle A novel art for binary pattern recognition
Matsunaga, G.
Ciencias Informáticas
Adaptive Resonance Theory
Binary Pattern Recognition
Pattern Classification
title_short A novel art for binary pattern recognition
title_full A novel art for binary pattern recognition
title_fullStr A novel art for binary pattern recognition
title_full_unstemmed A novel art for binary pattern recognition
title_sort A novel art for binary pattern recognition
dc.creator.none.fl_str_mv Matsunaga, G.
Rey, H. G.
Zanutto, S.
Cernuschi Frías, Bruno
author Matsunaga, G.
author_facet Matsunaga, G.
Rey, H. G.
Zanutto, S.
Cernuschi Frías, Bruno
author_role author
author2 Rey, H. G.
Zanutto, S.
Cernuschi Frías, Bruno
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Adaptive Resonance Theory
Binary Pattern Recognition
Pattern Classification
topic Ciencias Informáticas
Adaptive Resonance Theory
Binary Pattern Recognition
Pattern Classification
dc.description.none.fl_txt_mv Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequence of input patterns. In this work we explore its drawbacks and propose several modi cations. Comparativesimulations show the better performance of our algorithm.
Sociedad Argentina de Informática e Investigación Operativa
description Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequence of input patterns. In this work we explore its drawbacks and propose several modi cations. Comparativesimulations show the better performance of our algorithm.
publishDate 2002
dc.date.none.fl_str_mv 2002-09
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info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-1095
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/
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