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.
CITATION STYLE
Steidl, G. (2014). Supervised Learning by Support Vector Machines. In Handbook of Mathematical Methods in Imaging (pp. 1–54). Springer New York. https://doi.org/10.1007/978-3-642-27795-5_22-5
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