Support Vector Machines

Armin Shmilovici ()
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Armin Shmilovici: Ben-Gurion University

A chapter in Machine Learning for Data Science Handbook, 2023, pp 93-110 from Springer

Abstract: 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.

Keywords: Support vector machines; Margin classifier; Hyperplane classifiers; Support vector regression; Kernel methods (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24628-9_6

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