Machine learning methods for classification problems

• Authors: Groenitz Heiko
• Languages of publication: EN

Keywords

EN

algorithm; credit analysis; pattern recognition; random forest; support vector machine

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Śląski Przegląd Statystyczny
• Year: 2020
• Issue: 18 (24)
• Pages: 241-248

Contributors

author

Groenitz Heiko

• Philipps University of Marburg, Germany

References

• Breiman L. (2001), Random forests. Machine Learning Journal, 45, pp. 5-32.
• Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984), Classification and regression trees, Chapman & Hall/CRC, Boca Raton.
• Cortes C., Vapnik V.N. (1995), Support-vector networks, Machine Learning, 20, pp. 273-297.
• Hamel L. (2009), Knowledge Discovery with Support Vector Machines. John Wiley & Sons, Hoboken.
• Jobson J.D. (1992), Applied Multivariate Data Analysis – Volume II: Categorical and Multivariate Methods, Springer, New York.
• Lantz B. (2015), Machine Learning with R. Packt Publishing, Birmingham.
• Moguerza J.M., Munoz A. (2006), Support vector machines with applications, Statistical Science, 21, pp. 322-336.
• Pathak M.A. (2014), Beginning Data Science with R. Springer, Cham.