Significative learning using Alpha-Beta associative memories

Catalán Salgado Edgar Armando, Yáñez Márquez Cornelio, Figueroa Nazuno Jesus

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

The main goal in pattern recognition is to be able to recognize interest patterns, although these patterns might be altered in some way. Associative memories is a branch in AI that obtains one generalization per class from the initial data set. The main problem is that when generalization is performed much information is lost. This is mainly due to the presence of outliers and pattern distribution in space. It is believed that one generalization is not sufficient to keep the information necessary to achieve a good performance in the recall phase. This paper shows a way to prevent information loss and make more significative learning allowing better recalling results.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 17th Iberoamerican Congress, CIARP 2012, Proceedings
Páginas535-542
Número de páginas8
DOI
EstadoPublicada - 2012
Publicado de forma externa
Evento17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 - Buenos Aires, Argentina
Duración: 3 sep. 20126 sep. 2012

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7441 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
País/TerritorioArgentina
CiudadBuenos Aires
Período3/09/126/09/12

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