https://terms-en.ai-term-hub.com/zh/tags/kernel-methods/Recent content in Kernel Methods 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/random_feature/Sat, 18 Jul 2026 11:31:50 +0000https://terms-en.ai-term-hub.com/zh/terms/random_feature/<h2 id="definition">Definition</h2> <p>随机特征映射将输入转换到新空间，使线性模型能够近似非线性核函数。这种方法通常与 Nyström 方法或傅里叶特征相关联，允许在保持计算效率的同时处理复杂的非线性关系。</p> <h3 id="summary">Summary</h3> <p>一种利用随机投影将输入数据映射到高维空间的技术，以高效近似核方法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>核近似</li> <li>特征映射</li> <li>计算效率</li> <li>线性化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>大规模核回归</li> <li>神经切线核 (NTK) 近似</li> <li>可扩展的高斯过程</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> numpy <span style="color:#66d9ef">as</span> np </span></span><span style="display:flex;"><span><span style="color:#f92672">from</span> sklearn.kernel_approximation <span style="color:#f92672">import</span> RBFSampler </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>transformer <span style="color:#f92672">=</span> RBFSampler(gamma<span style="color:#f92672">=</span><span style="color:#ae81ff">1</span>, n_components<span style="color:#f92672">=</span><span style="color:#ae81ff">50</span>, random_state<span style="color:#f92672">=</span><span style="color:#ae81ff">42</span>) </span></span><span style="display:flex;"><span>X_transformed <span style="color:#f92672">=</span> transformer<span style="color:#f92672">.</span>fit_transform(X) </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%A0%B8%E6%8A%80%E5%B7%A7-kernel-trick/">核技巧 (Kernel trick)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%82%85%E9%87%8C%E5%8F%B6%E7%89%B9%E5%BE%81-fourier-features/">傅里叶特征 (Fourier features)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/nystr%C3%B6m-%E6%96%B9%E6%B3%95-nystrom-method/">Nyström 方法 (Nystrom method)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%99%8D%E7%BB%B4-dimensionality-reduction/">降维 (Dimensionality reduction)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/kernel_embedding_of_distributions/Sat, 18 Jul 2026 11:22:59 +0000https://terms-en.ai-term-hub.com/zh/terms/kernel_embedding_of_distributions/<h2 id="definition">Definition</h2> <p>分布的核嵌入允许将概率对象视为再生核希尔伯特空间（RKHS）中的点。通过将分布映射到高维特征空间，可以将复杂的概率比较问题转化为简单的向量代数运算，如计算均值嵌入之间的距离，从而实现对不同分布的量化分析和假设检验。</p> <h3 id="summary">Summary</h3> <p>一种将概率分布映射到再生核希尔伯特空间的技术，以便通过向量运算进行比较和操作。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>再生核希尔伯特空间</li> <li>均值嵌入</li> <li>非参数推断</li> <li>分布比较</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>两样本假设检验</li> <li>从观测数据中发现因果关系</li> <li>比较生成模型的输出分布</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%9C%80%E5%A4%A7%E5%9D%87%E5%80%BC%E5%B7%AE%E5%BC%82-maximum-mean-discrepancy/">最大均值差异 (Maximum Mean Discrepancy)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%B8%8C%E5%B0%94%E4%BC%AF%E7%89%B9%E7%A9%BA%E9%97%B4-hilbert-space/">希尔伯特空间 (Hilbert Space)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%AB%98%E6%96%AF%E8%BF%87%E7%A8%8B-gaussian-process/">高斯过程 (Gaussian Process)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%BB%9F%E8%AE%A1%E6%A3%80%E9%AA%8C-statistical-testing/">统计检验 (Statistical Testing)</a></li> </ul>