https://terms-en.ai-term-hub.com/zh/tags/ml-paradigms/Recent content in ML Paradigms on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/semi_supervised_learning/Sat, 18 Jul 2026 11:33:07 +0000https://terms-en.ai-term-hub.com/zh/terms/semi_supervised_learning/<h2 id="definition">Definition</h2> <p>半监督学习是一种混合训练范式，它利用少量标记数据和大量未标记数据。其核心假设是未标记数据的结构有助于学习决策边界。</p> <h3 id="summary">Summary</h3> <p>一种利用标记数据和未标记数据来提高模型准确性和泛化能力的机器学习方法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>标记数据</li> <li>未标记数据</li> <li>自训练</li> <li>流形假设</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>标注有限的图像分类</li> <li>标签稀疏的文本情感分析</li> <li>专家数据稀缺的医疗诊断预测</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/supervised-learning-%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">Supervised learning (监督学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/unsupervised-learning-%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">Unsupervised learning (无监督学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/active-learning-%E4%B8%BB%E5%8A%A8%E5%AD%A6%E4%B9%A0/">Active learning (主动学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/self-supervised-learning-%E8%87%AA%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">Self-supervised learning (自监督学习)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/online/Sat, 18 Jul 2026 10:53:26 +0000https://terms-en.ai-term-hub.com/zh/terms/online/<h2 id="definition">Definition</h2> <p>在线学习是一种机器学习范式，模型随着新数据点的到达而增量更新，而不是一次性在静态批量数据上进行训练。这种方法至关重要</p> <h3 id="summary">Summary</h3> <p>指机器学习模型能够从新数据流中实时持续学习，而无需从头重新训练。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>增量学习</li> <li>流式数据</li> <li>实时适应</li> <li>批量与在线</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>实时欺诈检测</li> <li>股票价格预测</li> <li>个性化推荐系统</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><span style="color:#f92672">from</span> sklearn.linear_model <span style="color:#f92672">import</span> SGDClassifier </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> SGDClassifier() </span></span><span style="display:flex;"><span><span style="color:#75715e"># Simulate online learning with partial_fit</span> </span></span><span style="display:flex;"><span>model<span style="color:#f92672">.</span>partial_fit(X_batch, y_batch, classes<span style="color:#f92672">=</span>[<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">1</span>]) </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/streaming_data-%E6%B5%81%E5%BC%8F%E6%95%B0%E6%8D%AE/">streaming_data (流式数据)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/incremental_learning-%E5%A2%9E%E9%87%8F%E5%AD%A6%E4%B9%A0/">incremental_learning (增量学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/real_time_processing-%E5%AE%9E%E6%97%B6%E5%A4%84%E7%90%86/">real_time_processing (实时处理)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/batch_learning-%E6%89%B9%E9%87%8F%E5%AD%A6%E4%B9%A0/">batch_learning (批量学习)</a></li> </ul>