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Support Vector Machines

  • Antonio Mucherino
  • Petraq J. Papajorgji
  • Panos M. Pardalos
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 34)

Abstract

Support vector machines (SVMs) are supervised learning methods used for classification [30, 41, 232]. This is one of the techniques among the top 10 for data mining [237]. In their basic form, SVMs are used for classifying sets of samples into two disjoint classes, which are separated by a hyperplane defined in a suitable space. Note that, as consequence, a single SVM can only discriminate between two different classifications. However, as we will discuss later, there are strategies that allow one to extend SVMs for classification problems with more than two classes [232, 220]. The hyperplane used for separating the two classes can be defined on the basis of the information contained in a training set.

Keywords

Support Vector Machine Kernel Function Bird Species Quadratic Programming Problem Dual Formulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Antonio Mucherino
    • 1
  • Petraq J. Papajorgji
    • 1
  • Panos M. Pardalos
    • 2
  1. 1.Institute of Food & Agricultural Information Technology OfficeUniversity of FloridaGainesvilleUSA
  2. 2.Department of Industrial & Systems EngineeringUniversity of FloridaGainesvilleUSA

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