<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Distribution on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/data-distribution/</link><description>Recent content in Data Distribution on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/data-distribution/index.xml" rel="self" type="application/rss+xml"/><item><title>Domain</title><link>https://terms-en.ai-term-hub.com/en/terms/domain/</link><pubDate>Sat, 18 Jul 2026 09:31:32 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/domain/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>In machine learning, particularly in transfer learning, a domain is defined by two components: the feature space (the set of all possible inputs) and the marginal probability distribution of those inputs. For example, images taken in daylight and images taken at night constitute different domains due to their distinct distributions, even if they share the same feature space (pixels). Understanding domains is critical for addressing domain shift, where a model trained on one domain performs poorly on another, necessitating techniques like domain adaptation to bridge the gap between source and target distributions.&lt;/p></description></item></channel></rss>