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dc.date.accessioned 2012-11-28T20:15:52Z
dc.date.available 2012-11-28T20:15:52Z
dc.date.issued 1998-11
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/24829
dc.description.abstract This paper presents a new approach to reduce the space problem due to combinatorial explosion of CNM (Combinatorial Neural Model) method. First we show a description of CNM, proposed by Machado and Rocha [MAC 91], [MAC 92], [MAC 92a], [MAC 97], as a variation of fuzzy neural network introduced as an alternative to meet many requirements, such as expressiveness, inteligibility, plasticity and flexibility. Our approach represents an alternative to generate the CNM network with certainty factors for each hypothesis. We demonstrate by means of a simple practical example that the number of combinations can be really reduced. en
dc.language en es
dc.subject Neural nets es
dc.subject data minning en
dc.subject Learning es
dc.subject knowledge discovery from databases en
dc.subject supervised learning en
dc.subject Data mining es
dc.subject hybrid systems en
dc.subject neural networks en
dc.title A parsimonious generation of combinatorial neural model en
dc.type Objeto de conferencia es
sedici.creator.person Prado, Hércules A. es
sedici.creator.person Frigeri, Sandra es
sedici.creator.person Engel, Paulo Martins es
sedici.description.note Sistemas Inteligentes es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.materias Informática es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en 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 1998-10
sedici.relation.event IV Congreso Argentina de Ciencias de la Computación 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)