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Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households

dc.contributor.authorGarcía Alonso, Carlos Ramón
dc.contributor.authorGuardiola, Jorge
dc.contributor.authorHervas Martínez, César
dc.date.accessioned2019-02-04T15:19:13Z
dc.date.available2019-02-04T15:19:13Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/20.500.12412/1026
dc.description.abstractA new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model).es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleLogistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan householdses
dc.typearticlees
dc.identifier.doi10.1016/j.ejor.2008.02.013
dc.issue.number2es
dc.journal.titleEuropean Journal Of Operational Researches
dc.page.initial543es
dc.page.final551es
dc.rights.accessRightsopenAccesses
dc.subject.keywordneural networkses
dc.subject.keywordlogistic regressiones
dc.subject.keywordproduct-unites
dc.subject.keywordevolutionary algorithmses
dc.subject.keywordsustainabilityes
dc.subject.keywordpoor householdses
dc.volume.number195es


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