8 years ago · bf3781cf1a
--- a/talks/svm-lightning/badline1.png
+++ b/talks/svm-lightning/badline1.png
--- a/talks/svm-lightning/badline2.png
+++ b/talks/svm-lightning/badline2.png
--- a/talks/svm-lightning/badset1.png
+++ b/talks/svm-lightning/badset1.png
--- a/talks/svm-lightning/badset2.png
+++ b/talks/svm-lightning/badset2.png
--- a/talks/svm-lightning/badset3.png
+++ b/talks/svm-lightning/badset3.png
--- a/talks/svm-lightning/basedata.png
+++ b/talks/svm-lightning/basedata.png
--- a/talks/svm-lightning/bordervectors.png
+++ b/talks/svm-lightning/bordervectors.png
--- a/talks/svm-lightning/cleansep.png
+++ b/talks/svm-lightning/cleansep.png
--- a/talks/svm-lightning/index.html
+++ b/talks/svm-lightning/index.html
@@ -0,0 +1,218 @@
 
				+<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
			
 
				+<html> <head>
			
 
				+<title>Support Vector Machines</title>
			
 
				+
			
 
				+<style>
			
 
				+
			
 
				+.slide {
			
 
				+  border: 2px solid #000066;
			
 
				+  background-color: #CCCCFF;
			
 
				+  position: absolute;
			
 
				+  padding: 5%;
			
 
				+  width: 85%;
			
 
				+  height: 80%;
			
 
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				+
			
 
				+
			
 
				+</style>
			
 
				+
			
 
				+<script>
			
 
				+var slides = new Array();
			
 
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				+function initializeSlideshow() {
			
 
				+  var divs = document.getElementsByTagName("div");
			
 
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				+</head>
			
 
				+
			
 
				+<body onload="initializeSlideshow();">
			
 
				+
			
 
				+
			
 
				+<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" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the first:</h1>
			
 
				+<img style="float: right;" src="badline2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset1.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset3.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Conveniently, Support Vector Machines address both of the problems i've identified.</h1>
			
 
				+<p></p>
			
 
				+</div>
			
 
				+
			
 
				+<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>
			
 
				+</div>
			
 
				+
			
 
				+<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>
			
 
				+</div>
			
 
				+
			
 
				+</body></html>
			
 
				+
			
--- a/talks/svm-lightning/supportvectors.png
+++ b/talks/svm-lightning/supportvectors.png
--- a/talks/svm-lightning/svm_lightning.html
+++ b/talks/svm-lightning/svm_lightning.html
@@ -0,0 +1,218 @@
 
				+<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
			
 
				+<html> <head>
			
 
				+<title>Support Vector Machines</title>
			
 
				+
			
 
				+<style>
			
 
				+
			
 
				+.slide {
			
 
				+  border: 2px solid #000066;
			
 
				+  background-color: #CCCCFF;
			
 
				+  position: absolute;
			
 
				+  padding: 5%;
			
 
				+  width: 85%;
			
 
				+  height: 80%;
			
 
				+}
			
 
				+
			
 
				+
			
 
				+</style>
			
 
				+
			
 
				+<script>
			
 
				+var slides = new Array();
			
 
				+var currentSlide = 0;
			
 
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				+  var divs = document.getElementsByTagName("div");
			
 
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				+  for (var i in slides) {
			
 
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				+}
			
 
				+</script>
			
 
				+</head>
			
 
				+
			
 
				+<body onload="initializeSlideshow();">
			
 
				+
			
 
				+
			
 
				+<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" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the first:</h1>
			
 
				+<img style="float: right;" src="badline2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset1.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset3.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Conveniently, Support Vector Machines address both of the problems i've identified.</h1>
			
 
				+<p></p>
			
 
				+</div>
			
 
				+
			
 
				+<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>
			
 
				+</div>
			
 
				+
			
 
				+<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>
			
 
				+</div>
			
 
				+
			
 
				+</body></html>
			
 
				+
			
--- a/talks/svm-lightning/svm_lightning.html.bak
+++ b/talks/svm-lightning/svm_lightning.html.bak
@@ -0,0 +1,216 @@
 
				+<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
			
 
				+<html> <head>
			
 
				+<title>Support Vector Machines</title>
			
 
				+
			
 
				+<style>
			
 
				+
			
 
				+.slide {
			
 
				+  border: 2px solid #000066;
			
 
				+  background-color: #CCCCFF;
			
 
				+  position: absolute;
			
 
				+  width: 95%;
			
 
				+  height: 95%;
			
 
				+}
			
 
				+
			
 
				+
			
 
				+</style>
			
 
				+
			
 
				+<script>
			
 
				+var slides = new Array();
			
 
				+var currentSlide = 0;
			
 
				+
			
 
				+function initializeSlideshow() {
			
 
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				+
			
 
				+
			
 
				+
			
 
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				+  }
			
 
				+}
			
 
				+</script>
			
 
				+</head>
			
 
				+
			
 
				+<body onload="initializeSlideshow();">
			
 
				+
			
 
				+
			
 
				+<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>
			
 
				+<p><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></p>
			
 
				+</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" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the first:</h1>
			
 
				+<img style="float: right;" src="badline2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset1.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset2.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Problem, the second:</h1>
			
 
				+<img style="float: right;" src="badset3.png" />
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Conveniently, Support Vector Machines address both of the problems i've identified.</h1>
			
 
				+<p></p>
			
 
				+</div>
			
 
				+
			
 
				+<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" />
			
 
				+<p><ul>
			
 
				+<li>Create "border" vectors, parallel to eachother, touching the outermost edge of each category dataset.</li>
			
 
				+</ul></p>
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Solution, the first:</h1>
			
 
				+<img style="float: right;" src="bordervectors.png" />
			
 
				+<p><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></p>
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Solution, the first:</h1>
			
 
				+<img style="float: right;" src="supportvectors.png" />
			
 
				+<p><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></p>
			
 
				+</div>
			
 
				+
			
 
				+<div class='slide'>
			
 
				+<h1>Solution, the first:</h1>
			
 
				+<img style="float: right;" src="supportvectors.png" />
			
 
				+<p><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></p>
			
 
				+</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>
			
 
				+<p><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></p>
			
 
				+</div>
			
 
				+
			
 
				+<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>
			
 
				+</div>
			
 
				+
			
 
				+</body></html>
			
--- a/talks/svm-lightning/svm_lightning.txt
+++ b/talks/svm-lightning/svm_lightning.txt
@@ -0,0 +1,193 @@
 
				+Support Vector Machines
			
 
				+
			
 
				+(lightning talk)
			
 
				+
			
 
				+(LPW '07) (john melesky)
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Presupposing:
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+The problem: find a line that separates these two categories of thing
			
 
				+
			
 
				+<img style="float: right;" src="basedata.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+For humans, this is easy.
			
 
				+
			
 
				+<img style="float: right;" src="cleansep.png" />
			
 
				+
			
 
				+For mathematicians, it's actually not too hard.
			
 
				+
			
 
				+---
			
 
				+
			
 
				+For humans, this is easy.
			
 
				+
			
 
				+<img style="float: right;" src="cleansep.png" />
			
 
				+
			
 
				+For <del>mathematicians</del> computers, it's actually not too hard.
			
 
				+
			
 
				+---
			
 
				+
			
 
				+There are two problems, though.
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Problem, the first:
			
 
				+
			
 
				+<img style="float: right;" src="badline1.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Problem, the first:
			
 
				+
			
 
				+<img style="float: right;" src="badline2.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Problem, the second:
			
 
				+
			
 
				+<img style="float: right;" src="badset1.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Problem, the second:
			
 
				+
			
 
				+<img style="float: right;" src="badset2.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Problem, the second:
			
 
				+
			
 
				+<img style="float: right;" src="badset3.png" />
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Conveniently, Support Vector Machines address both of the problems i've identified.
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Solution, the first:
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Solution, the first:
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Solution, the first:
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Solution, the first:
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Solution, the first:
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+A joke:
			
 
				+
			
 
				+Q: How many mathematicians does it take to change a lightbulb?
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+A joke:
			
 
				+
			
 
				+Q: How many mathematicians does it take to change a lightbulb?
			
 
				+
			
 
				+A: One, who hands it to 127 Londoners, thus reducing it to an earlier joke.
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+A question:
			
 
				+
			
 
				+Q: How do mathematicians categorize non-linearly-separable data?
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+A question:
			
 
				+
			
 
				+Q: How do mathematicians categorize non-linearly-separable data?
			
 
				+
			
 
				+A: Munge the data until it's linearly separable, thus reducing it to an earlier slide.
			
 
				+
			
 
				+
			
 
				+---
			
 
				+
			
 
				+A question:
			
 
				+
			
 
				+Q: How do mathematicians categorize non-linearly-separable data?
			
 
				+
			
 
				+A: Munge the data until it's linearly separable, thus reducing it to an earlier slide.
			
 
				+
			
 
				+Seriously. The munging is done using what are known as "kernel methods".
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Kernel Methods
			
 
				+
			
 
				+<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>
			
 
				+
			
 
				+---
			
 
				+
			
 
				+Kernel Methods + Support Vectors = Support Vector Machines
			
 
				+
			
 
				+---
			
 
				+
			
 
				+In Perl:
			
 
				+
			
 
				+Algorithm::SVM - bindings to libsvm
			
 
				+
			
 
				+(Also wrapped by AI::Categorizer)
			
 
				+