<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on yhoka's blog</title><link>https://yhoka.com/en/tags/machine-learning/</link><description>Recent content in Machine Learning on yhoka's blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 04 Jan 2026 00:41:04 +0900</lastBuildDate><atom:link href="https://yhoka.com/en/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Elbow Method for k-means</title><link>https://yhoka.com/en/posts/k-means-elbow/</link><pubDate>Sat, 11 Jan 2025 12:17:04 +0900</pubDate><guid>https://yhoka.com/en/posts/k-means-elbow/</guid><description>Implemented the elbow method in C++ to estimate the number of clusters for k-means and verified its behavior.</description></item><item><title>k-means++</title><link>https://yhoka.com/en/posts/k-means++/</link><pubDate>Mon, 06 Jan 2025 11:50:59 +0900</pubDate><guid>https://yhoka.com/en/posts/k-means++/</guid><description>Implemented k-means++, an improved version of the k-means algorithm, in C++ and verified its behavior.</description></item><item><title>Implemented k-means in C++</title><link>https://yhoka.com/en/posts/k-means/</link><pubDate>Fri, 03 Jan 2025 22:22:49 +0900</pubDate><guid>https://yhoka.com/en/posts/k-means/</guid><description>Explored the behavior of k-means, perhaps the most well-known clustering algorithm, by implementing it in C++.</description></item></channel></rss>