2018-11-17 21:47:56 +08:00
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前言
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2018-11-16 00:23:54 +08:00
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====
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2018-11-16 00:19:34 +08:00
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2018-11-17 21:41:39 +08:00
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力求每行代码都有注释,重要部分注明公式来源。具体会追求下方这样的代码,学习者可以照着公式看程序,让代码有据可查。
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2018-11-17 21:30:09 +08:00
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2018-11-17 21:33:39 +08:00
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![image](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/CodePic.png)
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2018-11-16 00:19:34 +08:00
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2018-11-17 21:29:55 +08:00
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如果时间充沛的话,可能会试着给每一章写一篇博客。先放个博客链接吧:[传送门](http://www.pkudodo.com/)。
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2018-11-16 00:19:34 +08:00
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2018-11-16 00:49:38 +08:00
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##### 注:其中Mnist数据集已转换为csv格式,由于体积为107M超过限制,改为压缩包形式。下载后务必先将Mnist文件内压缩包直接解压。
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2018-11-16 00:19:34 +08:00
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2018-11-17 21:48:41 +08:00
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2018-11-17 21:47:56 +08:00
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实现
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======
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2018-11-16 00:19:34 +08:00
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2018-11-16 00:23:54 +08:00
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### 第二章 感知机:
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2018-11-18 01:25:16 +08:00
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博客:[统计学习方法|感知机原理剖析及实现](http://www.pkudodo.com/2018/11/18/%e7%bb%9f%e8%ae%a1%e5%ad%a6%e4%b9%a0%e6%96%b9%e6%b3%95%e6%84%9f%e7%9f%a5%e6%9c%ba%e5%8e%9f%e7%90%86%e5%8f%8a%e5%ae%9e%e7%8e%b0%e5%89%96%e6%9e%90/)
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2018-11-18 01:24:23 +08:00
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实现:[Statistical-Learning-Method_Code/perceptron/perceptron_dichotomy.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/perceptron/perceptron_dichotomy.py)
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2018-11-16 00:19:34 +08:00
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2018-11-17 21:41:39 +08:00
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### 第三章 K近邻:
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2018-11-19 18:21:40 +08:00
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博客:[统计学习方法|K近邻原理剖析及实现](http://www.pkudodo.com/2018/11/19/%e7%bb%9f%e8%ae%a1%e5%ad%a6%e4%b9%a0%e6%96%b9%e6%b3%95k%e8%bf%91%e9%82%bb%e5%8e%9f%e7%90%86%e5%89%96%e6%9e%90%e5%8f%8a%e5%ae%9e%e8%b7%b5/?preview_id=194&preview_nonce=a52c4627bb&_thumbnail_id=-1&preview=true)
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2018-11-16 21:50:35 +08:00
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[Statistical-Learning-Method_Code/KNN/KNN.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/KNN/KNN.py)
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2018-11-16 21:49:53 +08:00
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2018-11-17 21:41:39 +08:00
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### 第四章 朴素贝叶斯:
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[Statistical-Learning-Method_Code/NaiveBayes/NaiveBayes.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/NaiveBayes/NaiveBayes.py)
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