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Autores:
Carlos Fernandez-Lozano, Cristina Canto, Marcos Gestal, José Manuel Andrade, Juan Ramón Rabuñal, Julián Dorado, Alejandro Pazos
Título: Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification
Revista: The Scientific World Journal. Issue: Recent Advances on Bioinspired Computation
ISSN: 1537-744X
Volumen: 2013. ID 982438
Páginas: 1-13
Editorial: Hindawi Publishing Corporation
Fecha Publicación: Diciembre 2013
Factor de Impacto: 1.219
SCIMago Journal Rank: 0.510
PubMed ID: 24453933
doi: http://dx.doi.org/10.1155/2013/982438
Categorías WoS: Multidisciplinary Sciences - Cuartil: Q2 - Tercil: T1 - Posición 16 de 55
Citas ISI: 1
Citas Scopus: 1
Citas Google Scholar: 2

Abstract:

Given the background of the use of Neural Networks in problems of apple juice classification, this paper is aimed at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

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