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

A 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 hi...

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Detalles Bibliográficos
Autores: García Alonso, Carlos, Guardiola, Jorge, Hervás Martínez, César
Tipo de recurso: artículo
Fecha de publicación:2009
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/1026
Acceso en línea:http://hdl.handle.net/20.500.12412/1026
Access Level:acceso abierto
Palabra clave:Neural networks
Logistic regression
Product-unit
Evolutionary algorithms
Sustainability
Poor households
Descripción
Sumario:A 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).