Support Vector Machines

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

Abstract

Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers 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. Here, we provide several formulations, and discuss some key concepts.
Original languageEnglish
Title of host publicationData Mining And Knowledge Discovery Handbook
EditorsO Maimon, L Rokach
Pages231-247
Number of pages17
Edition2
ISBN (Electronic)978-0-387-09823-4
DOIs
StatePublished - 7 Jul 2010

Keywords

  • Hyperplane Classifiers
  • Kernel Methods
  • Margin Classifier
  • Support Vector Machines
  • Support Vector Regression

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