Adaptation on 中文AI术语词典https://terms-en.ai-term-hub.com/zh/tags/adaptation/Recent content in Adaptation on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000元学习https://terms-en.ai-term-hub.com/zh/terms/meta_learning/Sat, 18 Jul 2026 11:26:00 +0000https://terms-en.ai-term-hub.com/zh/terms/meta_learning/<h2 id="definition">Definition</h2> <p>元学习专注于设计能够从先前任务中学习以改善在新颖、未见任务上表现的算法。它不是针对每个问题从头开始训练模型，而是优化模型的学习机制，使其具备快速适应新环境的能力。</p> <h3 id="summary">Summary</h3> <p>元学习，或称“学会学习”，是一种机器学习方法，使模型能够利用以往经验，以最少数据快速适应新任务。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>少样本学习</li> <li>迁移学习</li> <li>模型无关元学习</li> <li>任务分布</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>快速适应新客户偏好</li> <li>多变环境下的机器人控制</li> <li>有限患者数据的医疗诊断</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/transfer-learning-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/">Transfer Learning (迁移学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/few-shot-learning-%E5%B0%91%E6%A0%B7%E6%9C%AC%E5%AD%A6%E4%B9%A0/">Few-Shot Learning (少样本学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reinforcement-learning-%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">Reinforcement Learning (强化学习)</a></li> </ul>微调https://terms-en.ai-term-hub.com/zh/terms/fine/Sat, 18 Jul 2026 10:51:19 +0000https://terms-en.ai-term-hub.com/zh/terms/fine/<h2 id="definition">Definition</h2> <p>微调涉及采用在大型数据集上训练过的通用模型，并在较小、更专业的数据集上进行进一步训练，以提高其在特定任务上的性能。该技术利用了迁移学习的优势。</p> <h3 id="summary">Summary</h3> <p>微调是指使用额外数据将预训练的AI模型适应特定任务或领域的过程。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>迁移学习</li> <li>预训练模型</li> <li>领域适应</li> <li>权重调整</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>医学影像诊断</li> <li>法律文档分析</li> <li>自定义情感分析</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/transfer-learning-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/">Transfer Learning (迁移学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/pre-training-%E9%A2%84%E8%AE%AD%E7%BB%83/">Pre-training (预训练)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/adaptation-%E9%80%82%E5%BA%94/">Adaptation (适应)</a></li> </ul>演进的https://terms-en.ai-term-hub.com/zh/terms/evolving/Sat, 18 Jul 2026 10:51:07 +0000https://terms-en.ai-term-hub.com/zh/terms/evolving/<h2 id="definition">Definition</h2> <p>“演进”一词描述了动态的人工智能模型，这些模型经历持续的学习和适应，而不是在初始训练后保持静态。这一概念是终身学习和在线学习系统的核心。</p> <h3 id="summary">Summary</h3> <p>描述通过新数据或反馈持续适应和改进的人工智能系统或算法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>持续学习</li> <li>适应</li> <li>动态更新</li> <li>终身学习</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>更新用户偏好的推荐系统</li> <li>适应交通模式的自动驾驶汽车</li> <li>学习新诈骗手法的欺诈检测系统</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/online-learning-%E5%9C%A8%E7%BA%BF%E5%AD%A6%E4%B9%A0/">Online Learning (在线学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/drift-%E6%BC%82%E7%A7%BB/">Drift (漂移)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/adaptation-%E9%80%82%E5%BA%94/">Adaptation (适应)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/feedback-loop-%E5%8F%8D%E9%A6%88%E5%BE%AA%E7%8E%AF/">Feedback Loop (反馈循环)</a></li> </ul>上下文学习https://terms-en.ai-term-hub.com/zh/terms/in_context_learning/Sat, 18 Jul 2026 07:44:46 +0000https://terms-en.ai-term-hub.com/zh/terms/in_context_learning/<h2 id="definition">Definition</h2> <p>上下文学习（ICL）允许大型语言模型在不更新权重的情况下适应新任务。通过在提示上下文中提供输入-输出对，模型可以推断出模式并执行相应任务。</p> <h3 id="summary">Summary</h3> <p>一种模型通过观察提示中提供的示例来执行任务的技术。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>少样本学习</li> <li>零样本</li> <li>提示设计</li> <li>无权重适配</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>快速测试模型在新数据集上的能力</li> <li>对话代理中的动态任务切换</li> <li>无需重新训练即可原型化AI应用</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span>prompt <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Translate English to French:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">English: Hello</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">French: Bonjour</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">English: Cat</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">French:&#34;</span> </span></span><span style="display:flex;"><span>response <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>generate(prompt) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%8F%90%E7%A4%BA%E5%B7%A5%E7%A8%8B/">提示工程</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%B0%91%E6%A0%B7%E6%9C%AC/">少样本</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%9B%B6%E6%A0%B7%E6%9C%AC/">零样本</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%85%83%E5%AD%A6%E4%B9%A0/">元学习</a></li> </ul>