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Linear Support Vector Machines

  • M. N. MurtyEmail author
  • Rashmi Raghava
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Support vector machine (SVM) is the most popular classifier based on a linear discriminant function. It is ideally suited for binary classification. It has been studied extensively in several pattern recognition applications and in data mining. It has become a baseline standard for classification because of excellent software packages that have been developed systematically over the past three decades. In this chapter, we introduce SVM-based classification and some of the essential properties related to classification. Specifically we deal with linear SVM that is ideally suited to deal with linearly separable classes.

Keywords

Linear SVM Perceptron and SVM Maximum margin Dual problem Binary classifier Multiclass classification 

References

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia
  2. 2.IBM India Private LimitedBangaloreIndia

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