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Handbook of Mathematical Methods in Imaging pp 1393–1453Cite as

Supervised Learning by Support Vector Machines

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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.

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  1. Department of Mathematics, University of Kaiserslautern, Kaiserslautern, Germany

    Gabriele Steidl

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  1. Computational Science Center, University of Vienna, Vienna, Austria

    Otmar Scherzer

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Steidl, G. (2015). Supervised Learning by Support Vector Machines. In: Scherzer, O. (eds) Handbook of Mathematical Methods in Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0790-8_22

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