<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Theory-Driven on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/theory-driven/</link><description>Recent content in Theory-Driven 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/theory-driven/index.xml" rel="self" type="application/rss+xml"/><item><title>Explanation-based learning</title><link>https://terms-en.ai-term-hub.com/en/terms/explanation_based_learning/</link><pubDate>Sat, 18 Jul 2026 09:57:38 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/explanation_based_learning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>EBL combines symbolic reasoning with machine learning to accelerate the learning process. Instead of relying on large datasets, it takes a single positive example and uses a pre-existing domain theory to explain why the example belongs to the target concept. This explanation is then operationalized into a general rule that can be applied to future instances. It is particularly useful when data is scarce but domain knowledge is abundant, allowing for rapid acquisition of skills.&lt;/p></description></item></channel></rss>