<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Surrogate Models on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/surrogate-models/</link><description>Recent content in Surrogate Models on 中文AI术语词典</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 18 Jul 2026 11:44:45 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/zh/tags/surrogate-models/index.xml" rel="self" type="application/rss+xml"/><item><title>贝叶斯优化</title><link>https://terms-en.ai-term-hub.com/zh/terms/bayesian_optimization/</link><pubDate>Sat, 18 Jul 2026 11:08:51 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/bayesian_optimization/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>贝叶斯优化使用概率代理模型（通常为高斯过程）来建模目标函数。它采用采集函数来平衡探索与利用，从而高效地找到最优参数配置。&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>一种用于全局优化昂贵评估黑盒函数的顺序设计策略。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>代理模型&lt;/li>
&lt;li>采集函数&lt;/li>
&lt;li>探索与利用权衡&lt;/li>
&lt;li>黑盒优化&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>深度学习模型的超参数调优&lt;/li>
&lt;li>科学实验设计的优化&lt;/li>
&lt;li>机器人控制参数调整&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter_tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">hyperparameter_tuning (超参数调优)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/gaussian_processes-%E9%AB%98%E6%96%AF%E8%BF%87%E7%A8%8B/">gaussian_processes (高斯过程)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/acquisition_function-%E9%87%87%E9%9B%86%E5%87%BD%E6%95%B0/">acquisition_function (采集函数)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/grid_search-%E7%BD%91%E6%A0%BC%E6%90%9C%E7%B4%A2/">grid_search (网格搜索)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>