<?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 | Peilong Ma</title><link>https://mplebron.github.io/tags/machine-learning/</link><atom:link href="https://mplebron.github.io/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 26 Jan 2022 00:00:00 +0000</lastBuildDate><image><url>https://mplebron.github.io/media/icon_hu68170e94a17a2a43d6dcb45cf0e8e589_3079_512x512_fill_lanczos_center_3.png</url><title>Machine Learning</title><link>https://mplebron.github.io/tags/machine-learning/</link></image><item><title>Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models</title><link>https://mplebron.github.io/publication/pm25-ml-models/</link><pubDate>Wed, 26 Jan 2022 00:00:00 +0000</pubDate><guid>https://mplebron.github.io/publication/pm25-ml-models/</guid><description>&lt;p>This article evaluates multiple machine learning algorithms for PM2.5 estimation and shows how multi-source urban data can improve fine-grained spatiotemporal retrieval.&lt;/p></description></item></channel></rss>