Support Vector Machines

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationMachine Learning for Data Science Handbook
Subtitle of host publicationData Mining and Knowledge Discovery Handbook, Third Edition
PublisherSpringer International Publishing
Pages93-110
Number of pages18
ISBN (Electronic)9783031246289
ISBN (Print)9783031246272
DOIs
StatePublished - 1 Jan 2023

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

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