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Title: GitHub - TimeIsGoOn/Statistical-Learning-Method_Code: 手写实现李航《统计学习方法》书中全部算法 · GitHub

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X Title: GitHub - TimeIsGoOn/Statistical-Learning-Method_Code: 手写实现李航《统计学习方法》书中全部算法

Description: 手写实现李航《统计学习方法》书中全部算法. Contribute to TimeIsGoOn/Statistical-Learning-Method_Code development by creating an account on GitHub.

Open Graph Description: 手写实现李航《统计学习方法》书中全部算法. Contribute to TimeIsGoOn/Statistical-Learning-Method_Code development by creating an account on GitHub.

X Description: 手写实现李航《统计学习方法》书中全部算法. Contribute to TimeIsGoOn/Statistical-Learning-Method_Code development by creating an account on GitHub.

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lvtengchao@pku.edu.cn

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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#前言
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传送门	http://www.pkudodo.com/
	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#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
	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#实现
	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第二章-感知机
统计学习方法\|感知机原理剖析及实现	http://www.pkudodo.com/2018/11/18/1-4/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第三章-k近邻
统计学习方法\|K近邻原理剖析及实现	http://www.pkudodo.com/2018/11/19/1-2/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第四章-朴素贝叶斯
统计学习方法\|朴素贝叶斯原理剖析及实现	http://www.pkudodo.com/2018/11/21/1-3/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第五章-决策树
统计学习方法\|决策树原理剖析及实现	http://www.pkudodo.com/2018/11/30/1-5/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第六章-逻辑斯蒂回归与最大熵模型
统计学习方法\|逻辑斯蒂原理剖析及实现	http://www.pkudodo.com/2018/12/03/1-6/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第七章-支持向量机
统计学习方法\|支持向量机(SVM)原理剖析及实现	http://www.pkudodo.com/2018/12/16/1-8/
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第八章-提升方法
AdaBoost/AdaBoost.py	https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/AdaBoost/AdaBoost.py
	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第九章-em算法及其推广
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	https://github.com/TimeIsGoOn/Statistical-Learning-Method_Code#第十章-隐马尔可夫模型
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