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English Information

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Authors
# Name
1 Bruno Guilherme Gomes(brunoguilherme@dcc.ufmg.br)
2 Mário Moreira(mcesar@dcc.ufmg.br)
3 Pedro Holanda(holanda@dcc.ufmg.br)

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Reference
# Reference
1 Constantinou, A. C., Fenton, N. E., and Neil, M. (2013). Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using bayesian networks. Knowledge-Based Systems, 50:60–86.
2 Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
3 Cover, T. M. and Thomas, J. A. (2006). Elements of information theory 2nd edition. Wiley-interscience.
4 Cui, T., Li, J., Woodward, J. R., and Parkes, A. J. (2013). An ensemble based genetic programming system to predict english football premier league games. In Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on, pages 138–143. IEEE.
5 Dixon, M. J. and Coles, S. G. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2):265–280.
6 Hucaljuk, J. and Rakipović, A. (2011). Predicting football scores using machine learning techniques. In MIPRO, 2011 Proceedings of the 34th International Convention, pages 1623–1627. IEEE.
7 Joseph, A., Fenton, N. E., and Neil, M. (2006). Predicting football results using bayesian nets and other machine learning techniques. Knowledge-Based Systems, 19(7):544–553.
8 McCullagh, P. and Nelder, J. A. (1989). Generalized linear models, volume 37. CRC press.
9 Tsakonas, A., Dounias, G., Shtovba, S., and Vivdyuk, V. (2002). Soft computing-based result prediction of football games. In The First International Conference on Inductive Modelling (ICIM’2002). Lviv, Ukraine. Citeseer.