<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Nearest Neighbor | L.E.R Academic</title><link>https://rongyi.ai/tag/nearest-neighbor/</link><atom:link href="https://rongyi.ai/tag/nearest-neighbor/index.xml" rel="self" type="application/rss+xml"/><description>Nearest Neighbor</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2022 Yi Rong</copyright><lastBuildDate>Sat, 29 Jan 2022 15:52:59 +0800</lastBuildDate><image><url>https://rongyi.ai/media/icon_hucf03f274847a1149dd55649cb0f12563_327173_512x512_fill_lanczos_center_3.png</url><title>Nearest Neighbor</title><link>https://rongyi.ai/tag/nearest-neighbor/</link></image><item><title>CSE251A Project 1: K-Means Clustering based Prototype Selection for Nearest Neighbor</title><link>https://rongyi.ai/report/cse251a-p1-report/</link><pubDate>Sat, 29 Jan 2022 15:52:59 +0800</pubDate><guid>https://rongyi.ai/report/cse251a-p1-report/</guid><description>&lt;p>In the report, we discuss our attempt to choose a better prototype for nearest neighbor other than random selection.We present a KMeans based prototype selection method that clearly outperforms the naive random selection in all our experiments.&lt;/p></description></item></channel></rss>