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search/search_index.json

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     <li class="md-nav__item">
-      <a href="../../../../../课程笔记/大三上/模式识别/3统计分类及其学习/i.md" class="md-nav__link">
+      <a href="../../3%E7%BB%9F%E8%AE%A1%E5%88%86%E7%B1%BB%E5%99%A8%E5%8F%8A%E5%85%B6%E5%AD%A6%E4%B9%A0/i/" class="md-nav__link">
 
 
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课程笔记/大三上/模式识别/0基本概念/i/index.html

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-      <a href="../../../../../课程笔记/大三上/模式识别/3统计分类及其学习/i.md" class="md-nav__link">
+      <a href="../../3%E7%BB%9F%E8%AE%A1%E5%88%86%E7%B1%BB%E5%99%A8%E5%8F%8A%E5%85%B6%E5%AD%A6%E4%B9%A0/i/" class="md-nav__link">
 
 
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@@ -2114,7 +2114,7 @@ <h2 id="_7">距离度量<a class="headerlink" href="#_7" title="Permanent link">
 <div class="arithmatex">\[d(\vec{x},\vec{y})=\left[\sum_{i=1}^{n}|x_i-y_i|^q\right]^{\frac{1}{q}}\]</div>
 <p><img alt="" src="../image-2.png" /></p>
 <p>马氏距离</p>
-<div class="arithmatex">\[D(x,y)=\sqrt{(\vec{x}-\vec{y})^T(\vec{x}-\vec{y})}\]</div>
+<div class="arithmatex">\[D(x,y)=\sqrt{(\vec{x}-\vec{y})^TC^{-1}(\vec{x}-\vec{y})}\]</div>
 <blockquote>
 <p>其中<span class="arithmatex">\(C=\frac{1}{n-1}\sum_{i=1}^{n}\left((x_i-\bar{x})(y_i-\bar{y})\right)^T\)</span>为多维特征协方差矩阵，如果各维特征独立同分布，那么马氏距离就是欧氏距离</p>
 </blockquote>

课程笔记/大三上/模式识别/1距离分类器/i/index.html

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@@ -2180,13 +2180,13 @@ <h2 id="_10">误差分析<a class="headerlink" href="#_10" title="Permanent link
 
 
 
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