<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Max-Margin on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/max-margin/</link><description>Recent content in Max-Margin on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 09 Jul 2026 21:02:19 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/max-margin/index.xml" rel="self" type="application/rss+xml"/><item><title>Support Vector Machines (SVMs)</title><link>https://abaj.ai/wiki/ml/supervised/classification/svm/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/supervised/classification/svm/</guid><description>&lt;p>a linearly separable dataset admits infinitely many separating hyperplanes, and the &lt;a
 href="https://abaj.ai/wiki/ml/supervised/classification/perceptron/"
 
 
>perceptron&lt;/a> will happily hand you whichever one it trips over first.&lt;span class="margin-note" data-note="the perceptron&amp;#39;s answer depends on initialisation and the order you feed it data. the svm&amp;#39;s answer is unique.">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
&lt;/span>

the support vector machine asks a better question: of all the hyperplanes that separate the data, which one is &lt;em>farthest from everybody&lt;/em>? the answer — the maximum-margin hyperplane — is determined by a handful of boundary points (the support vectors), drops out of a beautiful convex dual, and generalises via the kernel trick from lines to nearly anything.&lt;/p></description></item></channel></rss>