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• Duda, R. O., Hart, P. E. and Stork, D. G. Pattern classi…cation. 2nd. Edition. New York, Fan, L., and Yang, Y. A loss function analysis for classi…cation methods in text categorization. Proc. ICML,(2003), 472-479.
• Schapire, R. E., MSRI Workshop on Nonlinear Estimation and Classi…cation,The Boosting Approach to Machine Learning An Overview, 2002.
• Masnadi-Shirazi, H., Mahadevan, V., and Vasconcelos, N. (2010). On the design of robust classi…ers for computer vision. In Computer Vision and Pattern Recognition (CVPR), (2010)
• IEEE Conference on (pp. 779-786). Debruyne, M. Robust Support Vector Machine Classi…cation, Leuven Statistical Day,(2008).
• Ma, Y., Li, L., Huang, X. and Wang, S. . Robust Support Vector Machine Using Least Median Loss Penalty, Proceedings of the 18th IFAC World Congress, (2011).
• Wu, Y., Liu, Y. Robust Truncated-Hinge-Loss Support Vector, Journal of the American Statistical Association, (2007)Vol. 102, No. 479.
• Huang, X., Shi, L., and Suykens, J. A., Support vector machine classi…er with pinball loss. IEEE transactions on pattern analysis and machine intelligence, (2014), 36(5), 984-997.
• Masnadi-Shirazi, H. The Design Of Bayes Consistent Loss Functions For Classi…cation, Uni- versity of California, Phd Dissertation, San Dieago, 223 p, 2011.
• Kobetski, M. and Sullivan. J. Improved Boosting Performance by Exclusion of Ambiguous Positive Examples. In ICPRAM, (2013).
• Freund, Y., and Schapire, R. E. A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory,(1995),(pp. 37), Springer Berlin Heidelberg,
• Friedman, J., Hastie, T., and Tibshirani, R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, (2000), (2), 337-407.
• Sano, N., Suzuki, H., and Koda, M., A robust boosting method for mislabeled data. Journal of the Operations Research Society of Japan, (2004), 47(3), 182-196.
• Miao, Q., Cao, Y., Xia, G., Gong, M., Liu, J., and Song, J. , RBoost: label noise-robust boost- ing algorithm based on a nonconvex loss function and the numerically stable base learners. IEEE transactions on neural networks and learning systems, (2016), 27(11), 2216-2228.
• Kanamori, T., Takenouchi, T., Eguchi, S., and Murata, N., The most robust loss function for boosting, In Neural Information Processing, (2004), 496-501, Springer Berlin/Heidelberg.
• Rosset, S., Robust boosting and its relation to bagging, In Proceedings of the eleventh ACM SIGKDD international conference on Know ledge discovery in data mining, (2004), pp. 249
• Freund, Y., A more robust boosting algorithm, arXiv preprint arXiv:0905.2138, (2009).
• Kanamori, T., Takenouchi, T., Eguchi, S., and Murata, N., Robust loss functions for boosting. Neural computation, (2007), 19(8), 2183-2244.
• Savage, L. J. The Elicitation of Personal Probabilities and Expectations, Journal of American Statistical Association,(1971), 66:783-801.
• Dettling, M., and Bauhlmann, P., Boosting for tumor classi…cation with gene expression data. Bioinformatics, (2003), 19(9), 1061-1069.
• Tuszynski, J., http://svitsrv25.ep*.ch/R-doc/library/caTools/html/LogitBoost.html, 2013. Toka, O., Gudermannian LossFunction and Gudermannian BinaryClassi…cation
• Method,Hacettepe University PhD Thesis, 2016.
• Ocal, N., Ercan, M. K., and Kadioglu, E. Corporate Ratings and a Model Proposition for the Manufacturing Industry at Borsa Istanbul. International Journal of Financial Research, (2015), 6(3), 13.
• Lichman, M., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.
• Shapiro, A. D., Structured Induction in Expert Systems Addison, Wesley. This book is based on Shapiro’s Ph.D. thesis at the University of Edinburgh entitled "The Role of Structured Induction in Expert Systems". 1987.
• Asuncion, A., and Newman, D.J., UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2007.
• Quinlan, R., Simplifying decision trees, International Journal Man-Machine Studies, , (1987), , pp. 221-234.
• Current address : Onur Toka: Hacettepe University, Department of Statistics, 06800, Ankara, TURKEY.
• E-mail address : onur.toka@hacettepe.edu.tr ORCID: http://orcid.org/0000-0002-4025-4537
• Current address : Meral Cetin: Hacettepe University, Department of Statistics, 06800, Ankara, TURKEY.
• E-mail address : meral@hacettepe.edu.tr ORCID: http://orcid.org/0000-0003-0247-7120