<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RKHS on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/rkhs/</link><description>Recent content in RKHS on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 04 Dec 2021 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/rkhs/index.xml" rel="self" type="application/rss+xml"/><item><title>Kernel Methods (3): RKHS — The Theoretical Soul of Kernel Methods</title><link>https://www.chenk.top/en/kernel-methods/03-rkhs/</link><pubDate>Sat, 04 Dec 2021 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/kernel-methods/03-rkhs/</guid><description>&lt;p>If your eyes glaze over the moment a lecturer writes &amp;ldquo;RKHS&amp;rdquo; on the board, this part of the series is for you. RKHS is not a club of three intimidating letters — it is a function space, and once you see what lives inside it, kernel methods stop feeling like magic and start feeling like linear algebra you already know.&lt;/p>
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 &lt;img src="https://blog-pic-ck.oss-cn-beijing.aliyuncs.com/posts/kernel-methods/03-rkhs/fig1_hilbert_space_concept.png" alt="A Hilbert-space cover for Part 3 of the kernel-methods series" loading="lazy" decoding="async" class="content-image">
 
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