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dc.contributor.authorAguilar Castro, J.es_ES
dc.date.accessioned2017-06-16T22:02:14Z-
dc.date.available2017-06-16T22:02:14Z-
dc.date.issued2016-08-08es_ES
dc.identifier10.1080/08839514.2016.1213584es_ES
dc.identifier.isbn8839514es_ES
dc.identifier.other10.1080/08839514.2016.1213584es_ES
dc.identifier.urihttp://dspace.utpl.edu.ec/handle/123456789/18706-
dc.description.abstractIn this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the first, called Aprendizaje_nuevo, is used to learn new patterns and creates a new pattern recognition module in the model. The other, called Aprendizaje_por_refuerzo, is used to reinforce a pattern and adapts the module that represents the pattern to the changes in it. The algorithm is tested in various contexts (text and images) to analyze its capacities of learning and of recognition of the model. © 2016 Taylor & Francis.es_ES
dc.languageIngléses_ES
dc.subjectEngineering controlled terms: Character recognitiones_ES
dc.subjectPattern recognition Engineering main heading: Learning algorithmses_ES
dc.titleLearning Algorithm for the Recursive Pattern Recognition Modeles_ES
dc.typeArticlees_ES
dc.publisherApplied Artificial Intelligencees_ES
Appears in Collections:Artículos de revistas Científicas



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