Comparing the effectiveness of support vector machines with fuzzy, neuro-fuzzy and genetic programming approaches in result prediction of football games
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- @InProceedings{tsakonas_comparing_2002,
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author = "A. Tsakonas and G. Dounias and S. Shtovba and
V. Vivdyuk",
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title = "Comparing the effectiveness of support vector machines
with fuzzy, neuro-fuzzy and genetic programming
approaches in result prediction of football games",
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booktitle = "ICAIS-2002",
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year = "2002",
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address = "Crimea, Ucraine",
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month = sep,
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keywords = "genetic algorithms, genetic programming, fuzzy
techniques, genetic programming, neuro-fuzzy
approaches, result prediction of football games, soft
computing, support vector machines",
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URL = "http://mde-lab.aegean.gr/images/stories/docs/CC32.pdf",
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size = "3 pages",
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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.",
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notes = "http://mde-lab.aegean.gr/research-material",
- }
Genetic Programming entries for
Athanasios D Tsakonas
Georgios Dounias
Serhi D Shtovba
V Vivdyuk
Citations