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<title>Support Vector Machines</title>

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<div class='slide'>
<h1>Support Vector Machines</h1>
<p>(lightning talk)</p>
<p>(LPW '07) (john melesky)</p>
</div>

<div class='slide'>
<h1>Presupposing:</h1>
<ul>
<li>You have a bunch of something.</li>
<li>You can transform relevant attributes of those things into numbers.</li>
<li>You can connect those numbers into vectors (think coordinates in an attribute space).</li>
<li>You want to categorise them base on those numbers.</li>
</ul>
</div>

<div class='slide'>
<h1>The problem: find a line that separates these two categories of thing</h1>
<img style="float: right;" src="basedata.png" />
</div>

<div class='slide'>
<h1>For humans, this is easy.</h1>
<img style="float: right;" src="cleansep.png" />
<p>For mathematicians, it's actually not too hard.</p>
</div>

<div class='slide'>
<h1>For humans, this is easy.</h1>
<img style="float: right;" src="cleansep.png" />
<p>For <del>mathematicians</del> computers, it's actually not too hard.</p>
</div>

<div class='slide'>
<h1>There are two problems, though.</h1>
</div>

<div class='slide'>
<h1>Problem, the first:</h1>
<img style="float: right;" src="badline1.png" />
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<div class='slide'>
<h1>Problem, the first:</h1>
<img style="float: right;" src="badline2.png" />
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<div class='slide'>
<h1>Problem, the second:</h1>
<img style="float: right;" src="badset1.png" />
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<div class='slide'>
<h1>Problem, the second:</h1>
<img style="float: right;" src="badset2.png" />
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<div class='slide'>
<h1>Problem, the second:</h1>
<img style="float: right;" src="badset3.png" />
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<div class='slide'>
<h1>Conveniently, Support Vector Machines address both of the problems i've identified.</h1>
<p></p>
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<div class='slide'>
<h1>Solution, the first:</h1>
<p></p>
</div>

<div class='slide'>
<h1>Solution, the first:</h1>
<img style="float: right;" src="bordervectors.png" />
<ul>
<li>Create "border" vectors, parallel to eachother, touching the outermost edge of each category dataset.</li>
</ul>
</div>

<div class='slide'>
<h1>Solution, the first:</h1>
<img style="float: right;" src="bordervectors.png" />
<ul>
<li>Create "border" vectors, parallel to eachother, touching the outermost edge of each category dataset.</li>
<li>As you add new items, ensure these "borders" stay parallel.</li>
</ul>
</div>

<div class='slide'>
<h1>Solution, the first:</h1>
<img style="float: right;" src="supportvectors.png" />
<ul>
<li>Create "border" vectors, parallel to eachother, touching the outermost edge of each category dataset.</li>
<li>As you add new items, ensure these "borders" stay parallel.</li>
<li>Create your categorizing vector equidistant from your two "borders".</li>
</ul>
</div>

<div class='slide'>
<h1>Solution, the first:</h1>
<img style="float: right;" src="supportvectors.png" />
<ul>
<li>Create "border" vectors, parallel to eachother, touching the outermost edge of each category dataset.</li>
<li>As you add new items, ensure these "borders" stay parallel.</li>
<li>Create your categorizing vector equidistant from your two "borders".</li>
<li>These "borders" are called "support vectors".</li>
</ul>
</div>

<div class='slide'>
<h1>A joke:</h1>
<p>Q: How many mathematicians does it take to change a lightbulb?</p>
<p></p>
</div>

<div class='slide'>
<h1>A joke:</h1>
<p>Q: How many mathematicians does it take to change a lightbulb?</p>
<p>A: One, who hands it to 127 Londoners, thus reducing it to an earlier joke.</p>
<p></p>
</div>

<div class='slide'>
<h1>A question:</h1>
<p>Q: How do mathematicians categorize non-linearly-separable data?</p>
<p></p>
</div>

<div class='slide'>
<h1>A question:</h1>
<p>Q: How do mathematicians categorize non-linearly-separable data?</p>
<p>A: Munge the data until it's linearly separable, thus reducing it to an earlier slide.</p>
<p></p>
</div>

<div class='slide'>
<h1>A question:</h1>
<p>Q: How do mathematicians categorize non-linearly-separable data?</p>
<p>A: Munge the data until it's linearly separable, thus reducing it to an earlier slide.</p>
<p>Seriously. The munging is done using what are known as "kernel methods".</p>
</div>

<div class='slide'>
<h1>Kernel Methods</h1>
<ul>
<li>Functions that munge data</li>
<li>Very faintly magical (because i have no idea how they were derived)</li>
<li>Require some skill to choose the right one for the problem</li>
<ul>
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<div class='slide'>
<h1>Kernel Methods + Support Vectors = Support Vector Machines</h1>
</div>

<div class='slide'>
<h1>In Perl:</h1>
<p>Algorithm::SVM - bindings to libsvm</p>
<p>(Also wrapped by AI::Categorizer)</p>
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