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Robust classification and regression using support vector machines

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  • Trafalis, Theodore B.
  • Gilbert, Robin C.

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  • Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
    • Handle: RePEc:eee:ejores:v:173:y:2006:i:3:p:893-909
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    References listed on IDEAS

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    1. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
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    Cited by:

    1. Gianluca Gazzola & Myong K. Jeong, 2021. "Support vector regression for polyhedral and missing data," Annals of Operations Research, Springer, vol. 303(1), pages 483-506, August.
    2. Petros Xanthopoulos & Mario Guarracino & Panos Pardalos, 2014. "Robust generalized eigenvalue classifier with ellipsoidal uncertainty," Annals of Operations Research, Springer, vol. 216(1), pages 327-342, May.
    3. Peter Tsyurmasto & Michael Zabarankin & Stan Uryasev, 2014. "Value-at-risk support vector machine: stability to outliers," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 218-232, July.
    4. Mohammad Poursaeidi & O. Kundakcioglu, 2014. "Robust support vector machines for multiple instance learning," Annals of Operations Research, Springer, vol. 216(1), pages 205-227, May.
    5. Wenxin Zhu & Yunyan Song & Yingyuan Xiao, 2018. "A New Support Vector Machine Plus with Pinball Loss," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 52-70, April.
    6. Maryam Abaszade & Sohrab Effati, 2019. "A New Method for Classifying Random Variables Based on Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 152-174, April.
    7. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
    8. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    9. Ximing Wang & Neng Fan & Panos M. Pardalos, 2018. "Robust chance-constrained support vector machines with second-order moment information," Annals of Operations Research, Springer, vol. 263(1), pages 45-68, April.
    10. Chen, Yan-Cheng & Su, Chao-Ton, 2016. "Distance-based margin support vector machine for classification," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 141-152.
    11. Miyashiro, Ryuhei & Takano, Yuichi, 2015. "Mixed integer second-order cone programming formulations for variable selection in linear regression," European Journal of Operational Research, Elsevier, vol. 247(3), pages 721-731.
    12. Takeda, Akiko & Kanamori, Takafumi, 2009. "A robust approach based on conditional value-at-risk measure to statistical learning problems," European Journal of Operational Research, Elsevier, vol. 198(1), pages 287-296, October.
    13. Hossein Kamalzadeh & Saeid Nassim Sobhan & Azam Boskabadi & Mohsen Hatami & Amin Gharehyakheh, 2019. "Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines," Papers 1912.02373, arXiv.org.
    14. Couellan, Nicolas & Wang, Wenjuan, 2017. "Uncertainty-safe large scale support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 215-230.
    15. Cassioli, A. & Chiavaioli, A. & Manes, C. & Sciandrone, M., 2013. "An incremental least squares algorithm for large scale linear classification," European Journal of Operational Research, Elsevier, vol. 224(3), pages 560-565.

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