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Introduction

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

Abstract

Support vector machines (SVMs) have been successfully used in a variety of data mining and machine learning applications. One of the most popular applications is pattern classification. SVMs are so well-known to the pattern classification community that by default, researchers in this area use them as baseline classifiers to establish the superiority of the classifier proposed by them. In this chapter, we introduce some of the important terms associated with support vector machines and a brief history of their evolution.

Keywords

Classification Representation Proximity function Classifiers 

References

  1. 1.
    Abe, S.: Support Vector Machines for Pattern Classification. Springer (2010)Google Scholar
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    Minsky, M.L., Papert, S.: Perceptrons: An Introduction To Computational Geometry. MIT Press (1969)Google Scholar
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    Murphy, K.P.: Machine Learning—A Probabilistic Perspective. MIT Press (2012)Google Scholar
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    Vapnik, V.: The Nature of Statistical Learning Theory. Springer (2000)Google Scholar
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    Wang, L.: Support Vector Machines: Theory and Applications. Springer (2005)Google Scholar

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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