https://terms-en.ai-term-hub.com/zh/tags/machine-learning/Recent content in Machine 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/underfitting/Sat, 18 Jul 2026 11:37:26 +0000https://terms-en.ai-term-hub.com/zh/terms/underfitting/<h2 id="definition">Definition</h2> <p>当统计模型或机器学习算法无法准确近似将输入映射到输出的函数时，就会发生欠拟合。这通常是因为模型对于数据而言过于简单所致。</p> <h3 id="summary">Summary</h3> <p>机器学习模型未能捕捉到训练数据潜在趋势的状态。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>偏差-方差权衡</li> <li>模型复杂度</li> <li>训练误差</li> <li>特征工程</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>诊断模型性能不佳</li> <li>调整超参数</li> <li>选择适当的算法</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <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/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/hyperparameter-tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">Hyperparameter Tuning (超参数调优)</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>https://terms-en.ai-term-hub.com/zh/terms/offline_learning/Sat, 18 Jul 2026 11:28:49 +0000https://terms-en.ai-term-hub.com/zh/terms/offline_learning/<h2 id="definition">Definition</h2> <p>也称为批量学习，离线学习涉及使用先前收集的固定数据集来训练机器学习模型。与在线学习不同，模型不会根据新数据实时更新其参数，而是定期重新训练。</p> <h3 id="summary">Summary</h3> <p>离线学习是一种训练范式，模型在静态数据集上进行训练，在学习阶段不与实时环境交互。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>批量训练</li> <li>静态数据集</li> <li>模型重训练</li> <li>计算效率</li> <li>历史数据</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>基于历史用户数据训练推荐系统</li> <li>利用过往交易构建欺诈检测模型</li> <li>为存档照片开发图像分类器</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/batch_processing-%E6%89%B9%E5%A4%84%E7%90%86/">batch_processing (批处理)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/model_training-%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83/">model_training (模型训练)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/data_pipeline-%E6%95%B0%E6%8D%AE%E6%B5%81%E6%B0%B4%E7%BA%BF/">data_pipeline (数据流水线)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/leave_one_out_cross_validation/Sat, 18 Jul 2026 11:24:07 +0000https://terms-en.ai-term-hub.com/zh/terms/leave_one_out_cross_validation/<h2 id="definition">Definition</h2> <p>留一法交叉验证（LOOCV）是k折交叉验证的一种特殊情况，其中k等于数据集中的样本数量。它提供了对模型性能的近乎无偏的估计，因为每次训练都使用了尽可能多的数据，从而最大限度地减少了偏差。然而，这种方法计算成本较高，因为需要为每个样本重新训练模型。</p> <h3 id="summary">Summary</h3> <p>一种严格的重采样技术，模型在除一个样本外的所有样本上进行训练，并在该单个保留样本上进行测试，对每个数据点重复此过程。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>重采样</li> <li>模型评估</li> <li>偏差-方差权衡</li> <li>计算成本</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>在小规模医疗数据集上评估模型</li> <li>数据稀缺时的超参数调优</li> <li>严格比较算法性能</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.model_selection <span style="color:#f92672">import</span> LeaveOneOut </span></span><span style="display:flex;"><span>loo <span style="color:#f92672">=</span> LeaveOneOut() </span></span><span style="display:flex;"><span><span style="color:#66d9ef">for</span> train_index, test_index <span style="color:#f92672">in</span> loo<span style="color:#f92672">.</span>split(X): </span></span><span style="display:flex;"><span> X_train, X_test <span style="color:#f92672">=</span> X[train_index], X[test_index] </span></span><span style="display:flex;"><span> y_train, y_test <span style="color:#f92672">=</span> y[train_index], y[test_index] </span></span><span style="display:flex;"><span> model<span style="color:#f92672">.</span>fit(X_train, y_train) </span></span><span style="display:flex;"><span> score <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>score(X_test, y_test) </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/k-fold-cross-validation-k%E6%8A%98%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81/">k-fold cross-validation (k折交叉验证)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/train_test_split-%E8%AE%AD%E7%BB%83%E9%9B%86-%E6%B5%8B%E8%AF%95%E9%9B%86%E5%88%92%E5%88%86/">train_test_split (训练集-测试集划分)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bootstrap-%E8%87%AA%E5%8A%A9%E6%B3%95/">bootstrap (自助法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/cross_validation_score-%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E5%BE%97%E5%88%86/">cross_validation_score (交叉验证得分)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/limited_memory_ai/Sat, 18 Jul 2026 11:24:07 +0000https://terms-en.ai-term-hub.com/zh/terms/limited_memory_ai/<h2 id="definition">Definition</h2> <p>有限记忆AI代表了AI能力的第二个层次，系统可以从历史数据中学习并相应地调整其行为。与反应式机器不同，这些系统保留有关先前交互的信息，并利用这些信息来改进未来的预测或决策，例如通过监督学习或强化学习算法。</p> <h3 id="summary">Summary</h3> <p>能够存储过去数据和经验以指导未来决策的人工智能系统，构成了大多数现代机器学习应用的基础。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>机器学习</li> <li>数据保留</li> <li>监督学习</li> <li>强化学习</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>垃圾邮件过滤</li> <li>医疗诊断辅助</li> <li>个性化内容推荐</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reactive_ai-%E5%8F%8D%E5%BA%94%E5%BC%8Fai/">reactive_ai (反应式AI)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/theory_of_mind-%E5%BF%83%E6%99%BA%E7%90%86%E8%AE%BA/">theory_of_mind (心智理论)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/self_aware_ai-%E8%87%AA%E6%88%91%E6%84%8F%E8%AF%86ai/">self_aware_ai (自我意识AI)</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> </ul>https://terms-en.ai-term-hub.com/zh/terms/lazy_learning/Sat, 18 Jul 2026 11:23:41 +0000https://terms-en.ai-term-hub.com/zh/terms/lazy_learning/<h2 id="definition">Definition</h2> <p>惰性学习者（如k近邻算法）会记住整个训练数据集，仅在做出预测时才进行计算。这与急切学习形成对比，后者会在训练阶段构建一个通用模型。</p> <h3 id="summary">Summary</h3> <p>一种学习方法，将泛化推迟到分类时才进行，存储训练实例而不是构建显式模型。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>基于实例的学习</li> <li>k近邻算法</li> <li>推理成本</li> <li>泛化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>推荐系统</li> <li>小数据集的模式识别</li> <li>预测模型的原型设计</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.neighbors <span style="color:#f92672">import</span> KNeighborsClassifier </span></span><span style="display:flex;"><span>clf <span style="color:#f92672">=</span> KNeighborsClassifier(n_neighbors<span style="color:#f92672">=</span><span style="color:#ae81ff">5</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/instance_based_learning-%E5%9F%BA%E4%BA%8E%E5%AE%9E%E4%BE%8B%E7%9A%84%E5%AD%A6%E4%B9%A0/">instance_based_learning (基于实例的学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/knn-k%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95/">knn (k近邻算法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/eager_learning-%E6%80%A5%E5%88%87%E5%AD%A6%E4%B9%A0/">eager_learning (急切学习)</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>https://terms-en.ai-term-hub.com/zh/terms/dataset_shift/Sat, 18 Jul 2026 11:12:42 +0000https://terms-en.ai-term-hub.com/zh/terms/dataset_shift/<h2 id="definition">Definition</h2> <p>当用于训练机器学习模型的数据分布与推理过程中遇到的数据分布不同时，就会发生数据集偏移。这种差异可能导致模型性能显著下降。</p> <h3 id="summary">Summary</h3> <p>数据集偏移是指训练数据与部署数据的输入统计特性发生变化的现象。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>协变量偏移</li> <li>概念漂移</li> <li>领域自适应</li> <li>泛化能力</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>监控生产环境中的机器学习模型</li> <li>重训练策略制定</li> <li>鲁棒性测试</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%BF%87%E6%8B%9F%E5%90%88-overfitting/">过拟合 (Overfitting)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%AC%A0%E6%8B%9F%E5%90%88-underfitting/">欠拟合 (Underfitting)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%A2%86%E5%9F%9F%E8%87%AA%E9%80%82%E5%BA%94-domain-adaptation/">领域自适应 (Domain Adaptation)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%95%B0%E6%8D%AE%E6%BC%82%E7%A7%BB-data-drift/">数据漂移 (Data Drift)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/data_augmentation/Sat, 18 Jul 2026 11:12:24 +0000https://terms-en.ai-term-hub.com/zh/terms/data_augmentation/<h2 id="definition">Definition</h2> <p>这种方法通过创建现有样本的修改版本来人工扩展训练数据集，例如旋转图像、在音频中添加噪声或在文本中进行同义词替换。它有助于防止……</p> <h3 id="summary">Summary</h3> <p>数据增强是一种通过变换现有数据点来增加训练数据集多样性和规模的技术。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>防止过拟合</li> <li>数据集扩展</li> <li>泛化能力</li> <li>变换</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>提高计算机视觉模型的鲁棒性</li> <li>在有限文本下增强自然语言处理模型的性能</li> <li>平衡不平衡的数据集</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> tensorflow.keras.preprocessing.image <span style="color:#f92672">import</span> ImageDataGenerator </span></span><span style="display:flex;"><span>gen <span style="color:#f92672">=</span> ImageDataGenerator(rotation_range<span style="color:#f92672">=</span><span style="color:#ae81ff">20</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/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/synthetic-data-%E5%90%88%E6%88%90%E6%95%B0%E6%8D%AE/">Synthetic Data (合成数据)</a></li> <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/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/cross_validation/Sat, 18 Jul 2026 11:12:12 +0000https://terms-en.ai-term-hub.com/zh/terms/cross_validation/<h2 id="definition">Definition</h2> <p>交叉验证是一种用于估计机器学习模型性能的统计方法。最常见的形式是k折交叉验证，即将数据分为k个相等的部分。模型在k-1个部分上进行训练，并在剩余的一个部分上进行测试，此过程重复k次，每次使用不同的部分作为测试集。</p> <h3 id="summary">Summary</h3> <p>一种重采样程序，通过将数据划分为子集进行训练和测试，用于在有限数据样本上评估机器学习模型。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>K折划分</li> <li>模型泛化能力</li> <li>过拟合检测</li> <li>性能估计</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>超参数调优</li> <li>比较不同算法</li> <li>在小数据集上验证模型稳定性</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.model_selection <span style="color:#f92672">import</span> cross_val_score </span></span><span style="display:flex;"><span>cv_scores <span style="color:#f92672">=</span> cross_val_score(model, X, y, cv<span style="color:#f92672">=</span><span style="color:#ae81ff">5</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/train-test-split-%E8%AE%AD%E7%BB%83-%E6%B5%8B%E8%AF%95%E9%9B%86%E5%88%92%E5%88%86/">Train-Test Split (训练-测试集划分)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/leave-one-out-%E7%95%99%E4%B8%80%E6%B3%95/">Leave-One-Out (留一法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bootstrap-%E8%87%AA%E5%8A%A9%E6%B3%95/">Bootstrap (自助法)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/concept_drift/Sat, 18 Jul 2026 11:11:03 +0000https://terms-en.ai-term-hub.com/zh/terms/concept_drift/<h2 id="definition">Definition</h2> <p>概念漂移是机器学习中的一种现象，即随着新数据的到来，输入特征与目标输出之间的关系会发生变化。这在用户行为动态变化的环境中经常发生。</p> <h3 id="summary">Summary</h3> <p>当目标变量的统计特性随时间发生变化时，就会发生概念漂移，从而导致模型性能下降。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>数据分布偏移</li> <li>模型退化</li> <li>在线学习</li> <li>重新训练</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>欺诈检测系统</li> <li>股票市场预测</li> <li>客户流失建模</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/data-drift-%E6%95%B0%E6%8D%AE%E6%BC%82%E7%A7%BB/">Data Drift (数据漂移)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/covariate-shift-%E5%8D%8F%E5%8F%98%E9%87%8F%E5%81%8F%E7%A7%BB/">Covariate Shift (协变量偏移)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/model-monitoring-%E6%A8%A1%E5%9E%8B%E7%9B%91%E6%8E%A7/">Model Monitoring (模型监控)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/adaptive-learning-%E8%87%AA%E9%80%82%E5%BA%94%E5%AD%A6%E4%B9%A0/">Adaptive Learning (自适应学习)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/ball_tree/Sat, 18 Jul 2026 11:08:27 +0000https://terms-en.ai-term-hub.com/zh/terms/ball_tree/<h2 id="definition">Definition</h2> <p>球树将数据点划分为嵌套的超球体（球），而不是超矩形。这种结构允许通过计算距离来进行高效的剪枝，从而加速最近邻查询。</p> <h3 id="summary">Summary</h3> <p>一种用于组织空间中点的二叉树数据结构，优化高维数据集中的最近邻搜索。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>超球体划分</li> <li>最近邻搜索</li> <li>高维数据</li> <li>树遍历</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>K近邻算法 (KNN)</li> <li>聚类分析</li> <li>异常检测</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.neighbors <span style="color:#f92672">import</span> BallTree </span></span><span style="display:flex;"><span><span style="color:#f92672">import</span> numpy <span style="color:#66d9ef">as</span> np </span></span><span style="display:flex;"><span>X <span style="color:#f92672">=</span> np<span style="color:#f92672">.</span>random<span style="color:#f92672">.</span>rand(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">5</span>) </span></span><span style="display:flex;"><span>tree <span style="color:#f92672">=</span> BallTree(X, metric<span style="color:#f92672">=</span><span style="color:#e6db74">&#39;euclidean&#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/kd-tree-k%E7%BB%B4%E6%A0%91/">KD-tree (K维树)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/k-nearest-neighbors-k%E8%BF%91%E9%82%BB/">K-Nearest Neighbors (K近邻)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/curse-of-dimensionality-%E7%BB%B4%E5%BA%A6%E7%81%BE%E9%9A%BE/">Curse of Dimensionality (维度灾难)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/anomaly_detection/Sat, 18 Jul 2026 11:04:49 +0000https://terms-en.ai-term-hub.com/zh/terms/anomaly_detection/<h2 id="definition">Definition</h2> <p>异常检测（也称为离群点检测）涉及分析数据以发现不符合预期行为的模式。它广泛应用于网络安全、欺诈检测和系统监控中。</p> <h3 id="summary">Summary</h3> <p>识别罕见项目、事件或观察结果的过程，这些结果与大多数数据显著偏离。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>离群点</li> <li>模式识别</li> <li>欺诈检测</li> <li>统计偏差</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>信用卡欺诈检测</li> <li>网络入侵检测</li> <li>工业故障诊断</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.ensemble <span style="color:#f92672">import</span> IsolationForest </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> IsolationForest(contamination<span style="color:#f92672">=</span><span style="color:#ae81ff">0.1</span>) </span></span><span style="display:flex;"><span>model<span style="color:#f92672">.</span>fit(data) </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/outlier-detection-%E7%A6%BB%E7%BE%A4%E7%82%B9%E6%A3%80%E6%B5%8B/">Outlier detection (离群点检测)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/machine-learning-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">Machine learning (机器学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/data-mining-%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98/">Data mining (数据挖掘)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/fraud-prevention-%E6%AC%BA%E8%AF%88%E9%A2%84%E9%98%B2/">Fraud prevention (欺诈预防)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/vector_database/Sat, 18 Jul 2026 10:55:52 +0000https://terms-en.ai-term-hub.com/zh/terms/vector_database/<h2 id="definition">Definition</h2> <p>向量数据库通过将非结构化数据转换为数值嵌入（embeddings）来优化其存储和检索。它们使用近似最近邻（ANN）等算法，以高效地查找相似性。</p> <h3 id="summary">Summary</h3> <p>一种专为存储、索引和查询表示数据特征的高维向量而设计的专用数据库。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>嵌入（Embeddings）</li> <li>相似度搜索</li> <li>高维空间</li> <li>ANN 索引</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>文档库中的语义搜索</li> <li>图像和音频识别系统</li> <li>个性化推荐引擎</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> pinecone </span></span><span style="display:flex;"><span>pinecone<span style="color:#f92672">.</span>init(api_key<span style="color:#f92672">=</span><span style="color:#e6db74">&#39;...&#39;</span>, environment<span style="color:#f92672">=</span><span style="color:#e6db74">&#39;...&#39;</span>) </span></span><span style="display:flex;"><span>index <span style="color:#f92672">=</span> pinecone<span style="color:#f92672">.</span>Index(<span style="color:#e6db74">&#39;my-index&#39;</span>) </span></span><span style="display:flex;"><span>result <span style="color:#f92672">=</span> index<span style="color:#f92672">.</span>query(vector<span style="color:#f92672">=</span>[<span style="color:#ae81ff">0.1</span>, <span style="color:#ae81ff">0.2</span>, <span style="color:#f92672">...</span>], top_k<span style="color:#f92672">=</span><span style="color:#ae81ff">5</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/embedding-%E5%B5%8C%E5%85%A5/">Embedding (嵌入)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/neural-network-%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">Neural Network (神经网络)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/similarity-metric-%E7%9B%B8%E4%BC%BC%E5%BA%A6%E5%BA%A6%E9%87%8F/">Similarity Metric (相似度度量)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/pinecone-pinecone-%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93/">Pinecone (Pinecone 向量数据库)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/reinforcement_learning/Sat, 18 Jul 2026 10:54:14 +0000https://terms-en.ai-term-hub.com/zh/terms/reinforcement_learning/<h2 id="definition">Definition</h2> <p>强化学习（RL）是机器学习的一个分支，专注于智能体应如何在环境中采取行动以最大化累积奖励的概念。与监督学习不同，强化学习不依赖标记数据，而是通过试错和奖励信号进行学习。</p> <h3 id="summary">Summary</h3> <p>强化学习是机器学习的一种类型，智能体通过与环境交互并最大化奖励来学习做出决策。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>智能体（Agent）</li> <li>环境</li> <li>策略</li> <li>奖励函数</li> <li>状态空间</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>自动驾驶</li> <li>游戏AI（如AlphaGo）</li> <li>推荐系统</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/deep-q-networks-%E6%B7%B1%E5%BA%A6q%E7%BD%91%E7%BB%9C/">Deep Q-Networks（深度Q网络）</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/policy-gradient-%E7%AD%96%E7%95%A5%E6%A2%AF%E5%BA%A6/">Policy Gradient（策略梯度）</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/markov-decision-process-%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E5%86%B3%E7%AD%96%E8%BF%87%E7%A8%8B/">Markov Decision Process（马尔可夫决策过程）</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/reinforcement/Sat, 18 Jul 2026 10:54:14 +0000https://terms-en.ai-term-hub.com/zh/terms/reinforcement/<h2 id="definition">Definition</h2> <p>强化是一种基本的心理和计算机制，智能体的行为由后果塑造。在机器学习中，它涉及为正确的行为提供正向反馈（奖励），从而引导智能体学习最佳策略。</p> <h3 id="summary">Summary</h3> <p>强化是指通过奖励或惩罚来修改行为，以优化决策的过程。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>奖励信号</li> <li>反馈循环</li> <li>行为塑造</li> <li>优化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>机器人控制</li> <li>游戏智能体</li> <li>资源管理</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <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> <li><a href="https://terms-en.ai-term-hub.com/en/terms/rlhf-%E5%9F%BA%E4%BA%8E%E4%BA%BA%E7%B1%BB%E5%8F%8D%E9%A6%88%E7%9A%84%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">RLHF（基于人类反馈的强化学习）</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/q-learning-q%E5%AD%A6%E4%B9%A0/">Q-Learning（Q学习）</a></li> </ul>