Support Vector Machines

Antonio Mucherino (), Petraq J. Papajorgji () and Panos M. Pardalos ()
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Antonio Mucherino: University of Florida
Petraq J. Papajorgji: University of Florida
Panos M. Pardalos: University of Florida

Chapter Chapter 6 in Data Mining in Agriculture, 2009, pp 123-141 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-0-387-88615-2_6

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