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...
| Autores: | , , |
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| 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 |
| 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). |
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