https://terms-en.ai-term-hub.com/zh/tags/supervised-learning/Recent content in Supervised Learning 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/labeled_data/Sat, 18 Jul 2026 11:23:41 +0000https://terms-en.ai-term-hub.com/zh/terms/labeled_data/<h2 id="definition">Definition</h2> <p>标注数据由输入样本及其对应的真实标签组成，是监督机器学习的基础。它允许算法学习输入与输出之间的映射关系。</p> <h3 id="summary">Summary</h3> <p>在输入特征旁边提供正确输出或目标值的数据。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>监督学习</li> <li>真实标签</li> <li>标注</li> <li>目标变量</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练图像分类器</li> <li>构建语音识别系统</li> <li>金融领域的预测建模</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> pandas <span style="color:#66d9ef">as</span> pd </span></span><span style="display:flex;"><span><span style="color:#75715e"># Example of loading labeled data</span> </span></span><span style="display:flex;"><span>df <span style="color:#f92672">=</span> pd<span style="color:#f92672">.</span>read_csv(<span style="color:#e6db74">&#39;train.csv&#39;</span>) </span></span><span style="display:flex;"><span>X <span style="color:#f92672">=</span> df<span style="color:#f92672">.</span>drop(<span style="color:#e6db74">&#39;label&#39;</span>, axis<span style="color:#f92672">=</span><span style="color:#ae81ff">1</span>) </span></span><span style="display:flex;"><span>y <span style="color:#f92672">=</span> df[<span style="color:#e6db74">&#39;label&#39;</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/unlabeled_data-%E6%9C%AA%E6%A0%87%E6%B3%A8%E6%95%B0%E6%8D%AE/">unlabeled_data (未标注数据)</a></li> <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/data_annotation-%E6%95%B0%E6%8D%AE%E6%A0%87%E6%B3%A8/">data_annotation (数据标注)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/training_set-%E8%AE%AD%E7%BB%83%E9%9B%86/">training_set (训练集)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/empirical_risk_minimization/Sat, 18 Jul 2026 11:16:12 +0000https://terms-en.ai-term-hub.com/zh/terms/empirical_risk_minimization/<h2 id="definition">Definition</h2> <p>经验风险最小化（ERM）是训练监督学习模型的标准目标函数。它涉及从一组函数中选择假设，以最小化训练数据上的平均误差（即经验风险）。其核心思想是通过优化训练集上的表现来逼近真实的风险分布。</p> <h3 id="summary">Summary</h3> <p>机器学习中的一个基本原则，旨在最小化观测训练数据上的平均损失。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>损失函数</li> <li>训练误差</li> <li>泛化能力</li> <li>过拟合</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练逻辑回归模型</li> <li>优化神经网络权重</li> <li>支持向量机构建</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/structural-risk-minimization-%E7%BB%93%E6%9E%84%E9%A3%8E%E9%99%A9%E6%9C%80%E5%B0%8F%E5%8C%96/">Structural risk minimization (结构风险最小化)</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/loss-function-%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0/">Loss function (损失函数)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bias-variance-tradeoff-%E5%81%8F%E5%B7%AE-%E6%96%B9%E5%B7%AE%E6%9D%83%E8%A1%A1/">Bias-variance tradeoff (偏差-方差权衡)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/data_annotation/Sat, 18 Jul 2026 11:12:24 +0000https://terms-en.ai-term-hub.com/zh/terms/data_annotation/<h2 id="definition">Definition</h2> <p>这一关键步骤涉及为原始数据点附加有意义的元数据，以便算法能够学习输入与输出之间的关系。例如，在图像中物体周围绘制边界框……</p> <h3 id="summary">Summary</h3> <p>数据标注是对原始数据（如图像或文本）进行标记的过程，使其适用于监督式机器学习训练。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>监督学习</li> <li>标记</li> <li>地面真值</li> <li>元数据</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练目标检测模型</li> <li>构建情感分析分类器</li> <li>创建语音识别数据集</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/ground-truth-%E5%9C%B0%E9%9D%A2%E7%9C%9F%E5%80%BC/">Ground Truth (地面真值)</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/crowdsourcing-%E4%BC%97%E5%8C%85/">Crowdsourcing (众包)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/labeled-data-%E6%A0%87%E6%B3%A8%E6%95%B0%E6%8D%AE/">Labeled Data (标注数据)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/binary_classification/Sat, 18 Jul 2026 11:09:15 +0000https://terms-en.ai-term-hub.com/zh/terms/binary_classification/<h2 id="definition">Definition</h2> <p>二分类是机器学习中一个基本问题，其输出变量是分类变量，恰好有两个可能的结果，例如真/假或垃圾邮件/非垃圾邮件。常用的算法包括逻辑回归、支持向量机等。</p> <h3 id="summary">Summary</h3> <p>一种监督学习任务，目标是为每个输入实例预测两个可能类别之一。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>决策边界</li> <li>混淆矩阵</li> <li>逻辑回归</li> <li>准确率指标</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>垃圾邮件检测</li> <li>医学诊断</li> <li>欺诈检测</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/multiclass-classification-%E5%A4%9A%E5%88%86%E7%B1%BB/">Multiclass Classification (多分类)</a></li> <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/roc-curve-roc%E6%9B%B2%E7%BA%BF/">ROC Curve (ROC曲线)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/precision-%E7%B2%BE%E7%A1%AE%E7%8E%87/">Precision (精确率)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/active_learning/Sat, 18 Jul 2026 11:04:13 +0000https://terms-en.ai-term-hub.com/zh/terms/active_learning/<h2 id="definition">Definition</h2> <p>主动学习通过允许模型选择最具信息量的实例进行人工标记，减少了所需标记数据的数量。与被动接收随机样本不同，算法会主动识别那些最能提升模型性能的数据点进行查询。</p> <h3 id="summary">Summary</h3> <p>一种机器学习策略，算法选择性地查询用户或专家来标记新数据点，从而优化训练过程。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>查询策略</li> <li>标记效率</li> <li>不确定性采样</li> <li>人在回路</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>由放射科专家进行医学图像标注</li> <li>稀有方言的情感分析标记</li> <li>自动驾驶场景的选择</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/query-strategies-%E6%9F%A5%E8%AF%A2%E7%AD%96%E7%95%A5/">query strategies (查询策略)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/data-annotation-%E6%95%B0%E6%8D%AE%E6%A0%87%E6%B3%A8/">data annotation (数据标注)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/label-efficiency-%E6%A0%87%E8%AE%B0%E6%95%88%E7%8E%87/">label efficiency (标记效率)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/multiple_instance_learning/Sat, 18 Jul 2026 11:01:16 +0000https://terms-en.ai-term-hub.com/zh/terms/multiple_instance_learning/<h2 id="definition">Definition</h2> <p>多示例学习（MIL）解决的是数据被分组为具有单一标签的“包”，而包内的各个实例未标记的场景。通常，如果一个包中包含至少一个正例实例，则该包被标记为正例；否则为负例。</p> <h3 id="summary">Summary</h3> <p>一种弱监督学习范式，标签被分配给实例集合（包），而非单个实例。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>包级标注</li> <li>实例级不确定性</li> <li>弱监督</li> <li>正/负包逻辑</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>药物活性预测</li> <li>基于边界框的图像分类</li> <li>基于内容的图像检索</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/weak_supervision-%E5%BC%B1%E7%9B%91%E7%9D%A3/">weak_supervision (弱监督)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bagging-%E8%A3%85%E8%A2%8B%E6%B3%95/">bagging (装袋法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/instance_classification-%E5%AE%9E%E4%BE%8B%E5%88%86%E7%B1%BB/">instance_classification (实例分类)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/label_noise-%E6%A0%87%E7%AD%BE%E5%99%AA%E5%A3%B0/">label_noise (标签噪声)</a></li> </ul>