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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Bibliographic Details
Authors: García Alonso, Carlos, Guardiola, Jorge, Hervás Martínez, César
Format: article
Publication Date:2009
Country:España
Institution:Universidad Loyola Andalucía
Repository:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/1026
Online Access:http://hdl.handle.net/20.500.12412/1026
Access Level:Open access
Keyword:Neural networks
Logistic regression
Product-unit
Evolutionary algorithms
Sustainability
Poor households
Description
Summary: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).