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dc.date.accessioned 2014-11-06T14:48:29Z
dc.date.available 2014-11-06T14:48:29Z
dc.date.issued 2014
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/42404
dc.description.abstract The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM). es
dc.language es es
dc.subject AHP es
dc.subject multicriteria decision marking system es
dc.subject SVM es
dc.subject BPN es
dc.title Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix es
dc.type Objeto de conferencia es
sedici.creator.person Favret, Fabián es
sedici.creator.person Labat, Marianela Daiana es
sedici.creator.person Rodriguez, Federico Matias es
sedici.description.note Eje: XV Workshop de Agentes y Sistemas Inteligentes es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras de Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2014-10
sedici.relation.event XX Congreso Argentino de Ciencias de la Computación (Buenos Aires, 2014) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)