Incorporating domain knowledge in machine learning for soccer outcome prediction

Author:

Berrar Daniel,Lopes Philippe,Dubitzky Werner

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference33 articles.

1. Angelini, G., & De Angelis, L. (2017). PARX model for football match predictions. Journal of Forecasting, 36(7), 795–807.

2. Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Instance-based concept learning from multiclass DNA microarray data. BMC Bioinformatics, 7(1), 73.

3. Brodley, C. E., & Smyth, P. (1997). Applying classification algorithms in practice. Statistics and Computing, 7(1), 45–56.

4. Chen, T., & Guestrin, C. (2016). XGBoost: Reliable large-scale tree boosting system. In: M. Shah, A. Smola, C. Aggarwal, D. Shen, & R. Rastogi (Eds.) Proceedings of the 22nd ACM SIGKDD conference on knowledge discovery and data mining, San Francisco, CA, USA (pp. 785–794).

5. Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2017). xgboost: Extreme gradient boosting. https://CRAN.R-project.org/package=xgboost , R package version 0.6-4. Further documentation at https://xgboost.readthedocs.io/en/latest/ . Accessed 24 July 2018.

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