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Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct... (Update)
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BibTeX entry: (Update)
C.J.C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):955-974, 1998. http://citeseer.ist.psu.edu/burges98tutorial.html More
@article{ burges98tutorial,
author = "Christopher J. C. Burges",
title = "A Tutorial on Support Vector Machines for Pattern Recognition",
journal = "Data Mining and Knowledge Discovery",
volume = "2",
number = "2",
pages = "121-167",
year = "1998",
url = "citeseer.ist.psu.edu/burges98tutorial.html" }
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