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Support Vector Machine

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Encyclopedia of Biometrics

Synonyms

Margin classifier; Maximum margin classifier; Optimal hyperplane SVM

Definition

Support vector machines (SVMs) are particular linear classifiers which are based on the margin maximization principle. They perform structural risk minimization, which improves the complexity of the classifier with the aim of achieving excellent generalization performance. The SVM accomplishes the classification task by constructing, in a higher dimensional space, the hyperplane that optimally separates the data into two categories.

Introduction

Considering a two-category classification problem, a linear classifier separates the space, with a hyperplane, into two regions, each of which is also called a class. Before the creation of SVMs, the popular algorithm for determining the parameters of a linear classifier was a single-neuron perceptron. The perceptron algorithm uses an updating rule to generate a separating surface for a two-class problem. The procedure is guaranteed to converge when the...

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References

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Author information

Authors and Affiliations

  1. ML-Consulting, Laval, QC, Canada

    Mathias M. Adankon

  2. University of Quebec, ETS, Montreal, Canada

    Mohamed Cheriet

Authors
  1. Mathias M. Adankon
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  2. Mohamed Cheriet
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Editor information

Editors and Affiliations

  1. Center for Biometrics and Security, Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

  2. Departments of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Anil K. Jain

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Adankon, M.M., Cheriet, M. (2015). Support Vector Machine. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_299

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