Supervised Learning by Support Vector Machines

Gabriele Steidl ()
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Gabriele Steidl: Department of Mathematics, University of Kaiserslautern

A chapter in Handbook of Mathematical Methods in Imaging, 2015, pp 1393-1453 from Springer

Abstract: Abstract During the last two decades, support vector machine learning has become a very active field of research with a large amount of both sophisticated theoretical results and exciting real-world applications. This paper gives a brief introduction into the basic concepts of supervised support vector learning and touches some recent developments in this broad field.

Keywords: Support Vector Machine (SVM); Hard Margin Classifier; Reproducing Kernel Hilbert Space (RKHS); Mercer Kernel; Positive Semi-definite (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-0790-8_22

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DOI: 10.1007/978-1-4939-0790-8_22

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