Support Vector Machines

Rudolf Mathar (), Gholamreza Alirezaei (), Emilio Balda and Arash Behboodi
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Rudolf Mathar: RWTH Aachen University, Institute for Theoretical Information Technology
Gholamreza Alirezaei: RWTH Aachen University, Chair and Institute for Communications Engineering
Emilio Balda: RWTH Aachen University, Institute for Theoretical Information Technology
Arash Behboodi: RWTH Aachen University, Institute for Theoretical Information Technology

Chapter Chapter 6 in Fundamentals of Data Analytics, 2020, pp 83-105 from Springer

Abstract: Abstract In 1992, Boser, Guyon and Vapnik [7] introduced a supervised algorithm for classification that after numerous extensions is now known as Support Vector Machines (SVMs). Support Vector Machines denotes a class of algorithms for classification and regression, which represent the current state of the art. The algorithm determines a small subset of points—the support vectors—in a Euclidean space such that a hyperplane determined solely by these vectors separates two large classes of points at its best. Support vector The purpose of this chapter is to introduce the key methodology based on convex optimization and kernel functions.

Date: 2020
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DOI: 10.1007/978-3-030-56831-3_6

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