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<!-- navigation toc: --> <li><a href="._week47-bs001.html#___sec0" style="font-size: 80%;">Overview of week 47</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#___sec6" style="font-size: 80%;">What is a hyperplane?</a></li>
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<h2 id="___sec4" class="anchor">Support Vector Machines, overarching aims </h2>
<p>
A Support Vector Machine (SVM) is a very powerful and versatile
Machine Learning method, capable of performing linear or nonlinear
classification, regression, and even outlier detection. It is one of
the most popular models in Machine Learning, and anyone interested in
Machine Learning should have it in their toolbox. SVMs are
particularly well suited for classification of complex but small-sized or
medium-sized datasets.
<p>
The case with two well-separated classes only can be understood in an
intuitive way in terms of lines in a two-dimensional space separating
the two classes.
<p>
The basic mathematics behind the SVM is however less familiar to most of us.
It relies on the definition of hyperplanes and the
definition of a <b>margin</b> which separates classes (in case of
classification problems) of variables. It is also used for regression
problems.
<p>
With SVMs we distinguish between hard margin and soft margins. The
latter introduces a so-called softening parameter to be discussed
below. We distinguish also between linear and non-linear
approaches. The latter are the most frequent ones since it is rather
unlikely that we can separate classes easily by say straight lines.
<p>
<p>
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