Comparing the effectiveness of support vector machines with fuzzy, neuro-fuzzy and genetic programming approaches in result prediction of football games

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{tsakonas_comparing_2002,

• author = "A. Tsakonas and G. Dounias and S. Shtovba and V. Vivdyuk",
• title = "Comparing the effectiveness of support vector machines with fuzzy, neuro-fuzzy and genetic programming approaches in result prediction of football games",
• booktitle = "ICAIS-2002",
• year = "2002",
• address = "Crimea, Ucraine",
• month = sep,
• keywords = "genetic algorithms, genetic programming, fuzzy techniques, genetic programming, neuro-fuzzy approaches, result prediction of football games, soft computing, support vector machines",
• URL = "http://mde-lab.aegean.gr/images/stories/docs/CC32.pdf",
• size = "3 pages",
• abstract = "A soft computing method for result prediction of football games based on machine learning techniques such as support vector machines is proposed in this article. The method is taking into account the following features of football terms: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed model achieves a satisfactory estimation of the actual game outcomes. The current work concludes with the recommendation of support vector machines technique as a powerful approach, for the creation of result prediction models of diverse sport championships.",
• notes = "http://mde-lab.aegean.gr/research-material",

}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias Serhi D Shtovba V Vivdyuk

Citations