https://terms-en.ai-term-hub.com/zh/tags/regularization/Recent content in Regularization 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/structural_risk_minimization/Sat, 18 Jul 2026 11:35:19 +0000https://terms-en.ai-term-hub.com/zh/terms/structural_risk_minimization/<h2 id="definition">Definition</h2> <p>结构风险最小化（SRM）是一种通过控制模型复杂度来防止过拟合、从而最小化期望风险的方法。它在经验风险最小化的基础上增加了正则化项，以惩罚过于复杂的模型。</p> <h3 id="summary">Summary</h3> <p>统计学习中的一项原则，旨在通过平衡模型拟合度和复杂度来最小化泛化误差的上界。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>VC维</li> <li>正则化</li> <li>泛化误差</li> <li>模型复杂度惩罚</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>支持向量机（SVM）训练</li> <li>回归分析中多项式阶数的选择</li> <li>剪枝决策树以避免过拟合</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%BB%8F%E9%AA%8C%E9%A3%8E%E9%99%A9%E6%9C%80%E5%B0%8F%E5%8C%96-empirical-risk-minimization-%E6%9C%80%E5%B0%8F%E5%8C%96%E8%AE%AD%E7%BB%83%E8%AF%AF%E5%B7%AE/">经验风险最小化 (Empirical risk minimization，最小化训练误差)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%A5%A5%E5%8D%A1%E5%A7%86%E5%89%83%E5%88%80-occam-s-razor-%E5%A6%82%E6%97%A0%E5%BF%85%E8%A6%81%E5%8B%BF%E5%A2%9E%E5%AE%9E%E4%BD%93%E7%9A%84%E7%AE%80%E7%BA%A6%E5%8E%9F%E5%88%99/">奥卡姆剃刀 (Occam&rsquo;s razor，如无必要勿增实体的简约原则)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%AD%A3%E5%88%99%E5%8C%96-regularization-%E9%80%9A%E8%BF%87%E6%B7%BB%E5%8A%A0%E7%BA%A6%E6%9D%9F%E9%98%B2%E6%AD%A2%E8%BF%87%E6%8B%9F%E5%90%88/">正则化 (Regularization，通过添加约束防止过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%81%8F%E5%B7%AE-%E6%96%B9%E5%B7%AE%E6%9D%83%E8%A1%A1-bias-variance-tradeoff-%E5%B9%B3%E8%A1%A1%E6%A8%A1%E5%9E%8B%E8%AF%AF%E5%B7%AE%E6%9D%A5%E6%BA%90/">偏差-方差权衡 (Bias-variance tradeoff，平衡模型误差来源)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/structured_sparsity_regularization/Sat, 18 Jul 2026 11:35:19 +0000https://terms-en.ai-term-hub.com/zh/terms/structured_sparsity_regularization/<h2 id="definition">Definition</h2> <p>结构化稀疏正则化扩展了标准的L1正则化，鼓励在特定模式中产生零值，而不是独立地对待各个系数。它融入了关于特征之间潜在结构或分组的先验知识。</p> <h3 id="summary">Summary</h3> <p>一种正则化技术，基于对数据中特征分组或结构的先验知识，强制实施特定的稀疏模式。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>组Lasso</li> <li>特征分组</li> <li>稀疏恢复</li> <li>先验知识整合</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>具有通路结构的基因表达分析</li> <li>具有块稀疏信号图像处理</li> <li>具有共享特征集的多任务学习</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/lasso%E5%9B%9E%E5%BD%92-lasso-regression-%E4%BD%BF%E7%94%A8l1%E6%AD%A3%E5%88%99%E5%8C%96%E7%9A%84%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/">Lasso回归 (Lasso regression，使用L1正则化的线性回归)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%BC%B9%E6%80%A7%E7%BD%91%E7%BB%9C-elastic-net-%E7%BB%93%E5%90%88l1%E5%92%8Cl2%E6%AD%A3%E5%88%99%E5%8C%96%E7%9A%84%E6%96%B9%E6%B3%95/">弹性网络 (Elastic net，结合L1和L2正则化的方法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%89%B9%E5%BE%81%E9%80%89%E6%8B%A9-feature-selection-%E4%BB%8E%E5%8E%9F%E5%A7%8B%E7%89%B9%E5%BE%81%E4%B8%AD%E6%8C%91%E9%80%89%E5%AD%90%E9%9B%86/">特征选择 (Feature selection，从原始特征中挑选子集)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%8E%8B%E7%BC%A9%E6%84%9F%E7%9F%A5-compressed-sensing-%E5%88%A9%E7%94%A8%E7%A8%80%E7%96%8F%E6%80%A7%E4%BB%8E%E5%B0%91%E9%87%8F%E6%B5%8B%E9%87%8F%E4%B8%AD%E9%87%8D%E5%BB%BA%E4%BF%A1%E5%8F%B7/">压缩感知 (Compressed sensing，利用稀疏性从少量测量中重建信号)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/matrix_regularization/Sat, 18 Jul 2026 11:25:36 +0000https://terms-en.ai-term-hub.com/zh/terms/matrix_regularization/<h2 id="definition">Definition</h2> <p>矩阵正则化将标量正则化的概念扩展到矩阵，常用于多任务学习或推荐系统。它对权重矩阵的范数施加约束，例如弗罗贝尼乌斯范数或核范数，以鼓励低秩近似或稀疏解，从而提高模型的泛化能力。</p> <h3 id="summary">Summary</h3> <p>一种将惩罚项应用于矩阵值参数的技术，以防止过拟合并强制执行稀疏性等结构属性。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>弗罗贝尼乌斯范数</li> <li>核范数</li> <li>防止过拟合</li> <li>低秩近似</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>协同过滤</li> <li>多任务学习</li> <li>降维</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/ridge-regression-%E5%B2%AD%E5%9B%9E%E5%BD%92/">Ridge Regression (岭回归)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/lasso-%E5%A5%97%E7%B4%A2%E5%9B%9E%E5%BD%92/">Lasso (套索回归)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/nuclear-norm-minimization-%E6%A0%B8%E8%8C%83%E6%95%B0%E6%9C%80%E5%B0%8F%E5%8C%96/">Nuclear Norm Minimization (核范数最小化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/sparse-learning-%E7%A8%80%E7%96%8F%E5%AD%A6%E4%B9%A0/">Sparse Learning (稀疏学习)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/manifold_regularization/Sat, 18 Jul 2026 11:25:36 +0000https://terms-en.ai-term-hub.com/zh/terms/manifold_regularization/<h2 id="definition">Definition</h2> <p>流形正则化通过结合数据分布的内在几何结构，扩展了传统的正则化方法。它基于以下假设：高维数据点实际上分布在低维流形上，因此模型应遵循该流形的几何特性以保持平滑性。</p> <h3 id="summary">Summary</h3> <p>一种半监督学习技术，假设数据位于低维流形上，并基于此几何结构对模型进行正则化。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>半监督学习</li> <li>数据流形</li> <li>图拉普拉斯算子</li> <li>平滑性先验</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>标签有限的文本分类</li> <li>图像识别任务</li> <li>生物医学数据分析</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/semi-supervised-learning-%E5%8D%8A%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">Semi-supervised learning (半监督学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/graph-based-learning-%E5%9F%BA%E4%BA%8E%E5%9B%BE%E7%9A%84%E5%AD%A6%E4%B9%A0/">Graph-based learning (基于图的学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/regularization-%E6%AD%A3%E5%88%99%E5%8C%96/">Regularization (正则化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/laplacian-eigenmaps-%E6%8B%89%E6%99%AE%E6%8B%89%E6%96%AF%E7%89%B9%E5%BE%81%E6%98%A0%E5%B0%84/">Laplacian Eigenmaps (拉普拉斯特征映射)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/early_stopping/Sat, 18 Jul 2026 11:15:46 +0000https://terms-en.ai-term-hub.com/zh/terms/early_stopping/<h2 id="definition">Definition</h2> <p>早停法是一种主要用于梯度下降等迭代训练过程中的正则化形式。在训练期间，模型在训练数据上的表现通常会持续改善，但在验证集上的表现可能会在某个点后开始恶化（表明出现过拟合）。早停法通过监控验证误差并在连续多个epoch未改善时停止训练来解决这一问题。</p> <h3 id="summary">Summary</h3> <p>早停法是一种正则化技术，当模型在验证集上的性能开始下降时停止训练过程，以防止过拟合。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>正则化</li> <li>验证集</li> <li>防止过拟合</li> <li>耐心参数</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>神经网络训练</li> <li>梯度提升算法</li> <li>时间序列预测模型</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/l2%E6%AD%A3%E5%88%99%E5%8C%96-%E6%9D%83%E9%87%8D%E8%A1%B0%E5%87%8F%E6%8A%80%E6%9C%AF/">L2正则化 (权重衰减技术)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/dropout-%E9%9A%8F%E6%9C%BA%E4%B8%A2%E5%BC%83%E7%A5%9E%E7%BB%8F%E5%85%83%E7%9A%84%E6%8A%80%E6%9C%AF/">Dropout (随机丢弃神经元的技术)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81-%E8%AF%84%E4%BC%B0%E6%A8%A1%E5%9E%8B%E6%B3%9B%E5%8C%96%E8%83%BD%E5%8A%9B%E7%9A%84%E6%8A%80%E6%9C%AF/">交叉验证 (评估模型泛化能力的技术)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%B3%9B%E5%8C%96%E8%AF%AF%E5%B7%AE-%E6%A8%A1%E5%9E%8B%E5%AF%B9%E6%9C%AA%E8%A7%81%E6%95%B0%E6%8D%AE%E9%A2%84%E6%B5%8B%E8%AF%AF%E5%B7%AE%E7%9A%84%E4%BC%B0%E8%AE%A1/">泛化误差 (模型对未见数据预测误差的估计)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/dropout/Sat, 18 Jul 2026 10:59:51 +0000https://terms-en.ai-term-hub.com/zh/terms/dropout/<h2 id="definition">Definition</h2> <p>在神经网络中，Dropout 通过在每次训练步骤中临时移除随机子集的神经元来防止过拟合。这迫使网络学习在联合使用时有用的鲁棒特征。</p> <h3 id="summary">Summary</h3> <p>Dropout 是一种正则化技术，通过在训练过程中随机忽略神经元来防止过拟合。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>正则化</li> <li>防止过拟合</li> <li>神经网络</li> <li>随机抑制</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练深层前馈神经网络</li> <li>提高大型语言模型的泛化能力</li> <li>减少对特定神经元路径的计算依赖</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">import</span> torch.nn <span style="color:#66d9ef">as</span> nn </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> nn<span style="color:#f92672">.</span>Sequential( </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Linear(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">50</span>), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Dropout(<span style="color:#ae81ff">0.5</span>), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>ReLU(), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Linear(<span style="color:#ae81ff">50</span>, <span style="color:#ae81ff">10</span>) </span></span><span style="display:flex;"><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/l2-regularization-l2%E6%AD%A3%E5%88%99%E5%8C%96-%E6%9D%83%E9%87%8D%E8%A1%B0%E5%87%8F/">L2 Regularization (L2正则化，权重衰减)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/batch-normalization-%E6%89%B9%E5%BD%92%E4%B8%80%E5%8C%96/">Batch Normalization (批归一化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/generalization-%E6%B3%9B%E5%8C%96/">Generalization (泛化)</a></li> </ul>