Consistency of support vector machines using additive kernels for additive models
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DOI: 10.1016/j.csda.2011.04.006
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Cited by:
- Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
- Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
- Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2019. "Individual-level social influence identification in social media: A learning-simulation coordinated method," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1005-1015.
- Tao, Yanfang & Song, Biqin & Li, Luoqing, 2018. "Error analysis for coefficient-based regularized regression in additive models," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 22-28.
- Jian Shi & Benlian Xu, 2016. "Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function," JRFM, MDPI, vol. 9(4), pages 1-10, November.
- Christophe Crambes & Ali Gannoun & Yousri Henchiri, 2014. "Modelling functional additive quantile regression using support vector machines approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 639-668, December.
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Keywords
Support vector machine; SVM; Additive model; Consistency; Robustness; Kernel;All these keywords.
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