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Linear programming approaches for multicategory support vector machines

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  • Yajima, Yasutoshi

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  • Yajima, Yasutoshi, 2005. "Linear programming approaches for multicategory support vector machines," European Journal of Operational Research, Elsevier, vol. 162(2), pages 514-531, April.
    • Handle: RePEc:eee:ejores:v:162:y:2005:i:2:p:514-531
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    Cited by:

    1. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
    2. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2010. "Learning the optimal kernel for Fisher discriminant analysis via second order cone programming," European Journal of Operational Research, Elsevier, vol. 203(3), pages 692-697, June.
    3. Uney, Fadime & Turkay, Metin, 2006. "A mixed-integer programming approach to multi-class data classification problem," European Journal of Operational Research, Elsevier, vol. 173(3), pages 910-920, September.
    4. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    5. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    6. Jun-ya Gotoh & Akiko Takeda & Rei Yamamoto, 2014. "Interaction between financial risk measures and machine learning methods," Computational Management Science, Springer, vol. 11(4), pages 365-402, October.

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