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Imbalance-oriented SVM methods for financial distress prediction : a comparative study among the new SB-SVM-ensemle method and traditional methods
Sun, Jie; Shang, Zhiming; Li, Hui - In: Journal of the Operational Research Society : OR 65 (2014) 12, pp. 1896-1904
Persistent link: https://www.econbiz.de/10010481864
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Robust Kriging models in computer experiments
Park, Taejin; Yum, Bongjin; Hung, Ying; Jeong, Young-Seon; … - In: Journal of the Operational Research Society : OR 67 (2016) 4, pp. 644-653
Persistent link: https://www.econbiz.de/10011516639
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Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines
López, Julio; Maldonado, Sebastián; Montoya, Ricardo - In: Journal of the Operational Research Society : OR 68 (2017) 11, pp. 1323-1334
Persistent link: https://www.econbiz.de/10011815891
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Financial risk forecasting with nonlinear dynamics and support vector regression
Tung, H. K. K.; Wong, Michael C. S. - In: Journal of the Operational Research Society : OR 60 (2009) 5, pp. 685-695
Persistent link: https://www.econbiz.de/10003872394
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Support vector machine learning with an evolutionary engine
Stoean, R.; Preuss, M.; Stoean, C.; El-Darzi, E.; … - In: Journal of the Operational Research Society : OR 60 (2009) 8, pp. 1116-1122
Persistent link: https://www.econbiz.de/10003882064
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Multiphase support vector regression for fuction approximation with break-points
Park, J. I.; Kim, N.; Jeong, M. K.; Shin, K. S. - In: Journal of the Operational Research Society : OR 64 (2013) 5, pp. 775-785
Persistent link: https://www.econbiz.de/10009741303