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<title>第 4 章 k邻近法 | 统计学习方法-读书笔记</title>
<meta name="description" content="最初看到 elegantbook 做的书籍样式很漂亮，就想把它引入到 bookdown 中，遂定制了此模版。在此基础上，做了迁移和扩展的工作，融合了 LaTeX (精美)、Pandoc (简洁) 和 R (强大) 的特性。This is a bookdown template based on ElegantBook. The output format for this template is bookdown::gitbook and bookdown::pdf_book." />
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<meta property="og:description" content="最初看到 elegantbook 做的书籍样式很漂亮，就想把它引入到 bookdown 中，遂定制了此模版。在此基础上，做了迁移和扩展的工作，融合了 LaTeX (精美)、Pandoc (简洁) 和 R (强大) 的特性。This is a bookdown template based on ElegantBook. The output format for this template is bookdown::gitbook and bookdown::pdf_book." />
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<meta name="twitter:title" content="第 4 章 k邻近法 | 统计学习方法-读书笔记" />
<meta name="twitter:description" content="最初看到 elegantbook 做的书籍样式很漂亮，就想把它引入到 bookdown 中，遂定制了此模版。在此基础上，做了迁移和扩展的工作，融合了 LaTeX (精美)、Pandoc (简洁) 和 R (强大) 的特性。This is a bookdown template based on ElegantBook. The output format for this template is bookdown::gitbook and bookdown::pdf_book." />
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<li><a href="./">Elegant Bookdown Template</a></li>
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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> 已有 Block</a>
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<li class="part"><span><b>I 监督学习</b></span></li>
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<li class="chapter" data-level="4" data-path="03-k-NN.html"><a href="03-k-NN.html"><i class="fa fa-check"></i><b>4</b> k邻近法</a></li>
<li class="chapter" data-level="5" data-path="04-naive-Bayes.html"><a href="04-naive-Bayes.html"><i class="fa fa-check"></i><b>5</b> 朴素贝叶斯</a></li>
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<li class="chapter" data-level="9" data-path="04-naive-Bayes.html"><a href="04-naive-Bayes.html#sec:naive-Bayes"><i class="fa fa-check"></i><b>9</b> 朴素贝叶斯</a></li>
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<li class="chapter" data-level="13" data-path="12-summary-Supervised.html"><a href="12-summary-Supervised.html"><i class="fa fa-check"></i><b>13</b> 监督学习方法总结</a></li>
<li class="part"><span><b>II 无监督学习</b></span></li>
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<li class="chapter" data-level="16" data-path="15-SVD.html"><a href="15-SVD.html"><i class="fa fa-check"></i><b>16</b> 奇异值分解</a></li>
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<li class="chapter" data-level="18" data-path="03-k-NN.html"><a href="03-k-NN.html#sec:k-NN"><i class="fa fa-check"></i><b>18</b> k邻近法</a></li>
<li class="chapter" data-level="19" data-path="18-PLSA.html"><a href="18-PLSA.html"><i class="fa fa-check"></i><b>19</b> 概率潜在语义分析</a></li>
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<li class="chapter" data-level="21" data-path="20-LDA.html"><a href="20-LDA.html"><i class="fa fa-check"></i><b>21</b> 潜在狄利克雷分布</a></li>
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<li class="chapter" data-level="23" data-path="22-summary-Unsupervised.html"><a href="22-summary-Unsupervised.html"><i class="fa fa-check"></i><b>23</b> 无监督学习方法总结</a></li>
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<li class="chapter" data-level="D" data-path="24-appendix.html"><a href="24-appendix.html#sec:matrix-subspace"><i class="fa fa-check"></i><b>D</b> 矩阵的基本子空间</a></li>
<li class="chapter" data-level="E" data-path="24-appendix.html"><a href="24-appendix.html#sec:KL-dirichlet-distribution"><i class="fa fa-check"></i><b>E</b> KL散度的定义和狄利克雷分布的性质</a></li>
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