<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Annotation on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/annotation/</link><description>Recent content in Annotation 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/annotation/index.xml" rel="self" type="application/rss+xml"/><item><title>Active learning</title><link>https://terms-en.ai-term-hub.com/en/terms/active_learning/</link><pubDate>Sat, 18 Jul 2026 09:44:54 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/active_learning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Active learning reduces the amount of labeled data required by allowing the model to choose the most informative instances for human labeling. Instead of passively receiving random samples, the algorithm identifies regions of high uncertainty or potential impact and requests labels specifically for those cases. This iterative process significantly lowers annotation costs and accelerates convergence, making it ideal for scenarios where data labeling is expensive, time-consuming, or requires specialized expertise.&lt;/p></description></item></channel></rss>