Learning Algorithm for the Recursive Pattern Recognition Model

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DC Field	Value	Language
dc.contributor.author	Aguilar Castro, J.	es_ES
dc.date.accessioned	2017-06-16T22:02:14Z	-
dc.date.available	2017-06-16T22:02:14Z	-
dc.date.issued	2016-08-08	es_ES
dc.identifier	10.1080/08839514.2016.1213584	es_ES
dc.identifier.isbn	8839514	es_ES
dc.identifier.other	10.1080/08839514.2016.1213584	es_ES
dc.identifier.uri	http://dspace.utpl.edu.ec/handle/123456789/18706	-
dc.description.abstract	In 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.language	Inglés	es_ES
dc.subject	Engineering controlled terms: Character recognition	es_ES
dc.subject	Pattern recognition Engineering main heading: Learning algorithms	es_ES
dc.title	Learning Algorithm for the Recursive Pattern Recognition Model	es_ES
dc.type	Article	es_ES
dc.publisher	Applied Artificial Intelligence	es_ES
Appears in Collections:	Artículos de revistas Científicas

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