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Support Vector Machines

In: Selected Applications of Convex Optimization

Author

Listed:
  • Li Li

    (Tsinghua University)

Abstract

In this chapter, we study support vector machines (SVM). We will see that optimization methodology plays an important role in building and training of SVM.

Suggested Citation

  • Li Li, 2015. "Support Vector Machines," Springer Optimization and Its Applications, in: Selected Applications of Convex Optimization, edition 127, chapter 0, pages 17-52, Springer.
    • Handle: RePEc:spr:spochp:978-3-662-46356-7_2
    DOI: 10.1007/978-3-662-46356-7_2
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    Cited by:

    1. Xu-Wen Wang & Lorenzo Madeddu & Kerstin Spirohn & Leonardo Martini & Adriano Fazzone & Luca Becchetti & Thomas P. Wytock & István A. Kovács & Olivér M. Balogh & Bettina Benczik & Mátyás Pétervári & Be, 2023. "Assessment of community efforts to advance network-based prediction of protein–protein interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Qi, Chengming & Wang, Yuping & Tian, Wenjie & Wang, Qun, 2016. "Multiple kernel boosting framework based on information measure for classification," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 175-186.
    3. Lei Ruan & Heng Liu, 2021. "Financial Distress Prediction Using GA-BP Neural Network Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(3), pages 1-1, March.
    4. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).

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