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Support Vector Machines

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Armin Shmilovici

    (Ben-Gurion University)

Abstract

Support vector machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. An SVM classifier creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. In this chapter, we provide several formulations and discuss some key concepts.

Suggested Citation

  • Armin Shmilovici, 2023. "Support Vector Machines," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 93-110, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_6
    DOI: 10.1007/978-3-031-24628-9_6
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