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https://github.com/deftruth/statistic-learning-r-note

📒《统计学习方法-李航: 笔记-从原理到实现，基于R语言》200页PDF，各种手推公式细节讲解，R语言实现. 🎉🎉
https://github.com/deftruth/statistic-learning-r-note

lihang ml r statistic-notes statistics statistics-learning

Last synced: 3 days ago
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📒《统计学习方法-李航: 笔记-从原理到实现，基于R语言》200页PDF，各种手推公式细节讲解，R语言实现. 🎉🎉

Host: GitHub
URL: https://github.com/deftruth/statistic-learning-r-note
Owner: DefTruth
License: gpl-3.0
Created: 2019-01-30T08:31:46.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2024-09-06T01:38:12.000Z (4 months ago)
Last Synced: 2024-12-21T21:05:58.951Z (10 days ago)
Topics: lihang, ml, r, statistic-notes, statistics, statistics-learning
Homepage:
Size: 23.3 MB
Stars: 424
Watchers: 4
Forks: 55
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

![image](https://github.com/DefTruth/statistic-learning-R-note/assets/31974251/07297b6a-d94c-4db0-aaef-8132071c94cb)

## News 👇👇
Most of my time now is focused on **LLM/VLM** Inference. Please check 📖[Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) ![](https://img.shields.io/github/stars/DefTruth/Awesome-LLM-Inference.svg?style=social), 📖[Awesome-SD-Inference](https://github.com/DefTruth/Awesome-SD-Inference) ![](https://img.shields.io/github/stars/DefTruth/Awesome-SD-Inference.svg?style=social) and 📖[CUDA-Learn-Notes](https://github.com/DefTruth/CUDA-Learn-Notes) ![](https://img.shields.io/github/stars/DefTruth/CUDA-Learn-Notes.svg?style=social) for more details.

## 📒Introduction
《统计学习方法-李航: 笔记-从原理到实现》这是一份非常详细的学习笔记，200页，各种手推公式细节讲解，整理成PDF，有详细的目录，可结合《统计学习方法》提高学习效率。如果觉得有用，不妨给个🌟Star支持一下吧~

## ©️Citations

```BibTeX
@misc{statistic-learning-R-note@2019,
title={statistic-learning-R-note: A detail note book of statistic-learning with R codes},
url={https://github.com/DefTruth/statistic-learning-R-note},
note={Open-source software available at https://github.com/DefTruth/statistic-learning-R-note},
author={DefTruth},
year={2019}
}
```

## 🎉Download PDFs
- [李航《统计学习方法》笔记--从原理到实现：基于R.pdf 👆🏻<点击下载!>](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
```shell
wget https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf
```

## 📖Contents

![image](https://github.com/DefTruth/statistic-learning-R-note/assets/31974251/561384a1-fbc3-40ed-af62-98268904f387)

- 第一章统计学习方法概述
- [1.6.2 泛化误差上界（P16-P17）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [1.4.2 过拟合与模型选择（P11）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第二章感知机
- [2.3.1 感知机算法的原始形式](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [2.3.2 算法的收敛性（Novikoff 定理）（P31-P33）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [2.3.3 感知机学习算法的对偶形式（P33-P34）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [2.3.1 感知机算法的原始形式（P28-P29）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [2.3.3 感知机学习算法的对偶形式（P33-P34）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第三章 K 近邻法
- [3.2.2 距离度量（P39）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [3.3.1 构造kd 树（P41-P42）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第四章朴素贝叶斯算法
- [4.1.1 基本方法（P47-P48）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [4.1.2 后验概率最大化的含义（P48-49）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [4.2.1 极大似然估计（P49）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [4.2.2 学习与分类算法（P50-51）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第五章决策树
- [5.2.2 信息增益（P60-P61）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [5.2.3 信息增益比（P63）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [5.3.1 ID3 算法/C4.5 算法（P63-P65）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [5.4 决策树的剪枝（P65-P67）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [5.5.1 CART 生成（P68-P71）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [5.5.2 CART 剪枝（P72-P73）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第六章逻辑斯蒂回归与最大熵模型
- [6.1.3 逻辑斯蒂回归模型的参数估计（P79）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [6.2.3 最大熵模型的学习（P83-P85）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [6.2.4 极大似然估计（P87）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [6.3.1 改进的迭代尺度算法（P89-P91）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第七章支持向量机
- [7.1.3 间隔最大化（P101）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [7.1.4 学习的对偶算法（P104）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [7.2.3 支持向量（P113）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [7.4 序列最小最优化算法（P126）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第八章提升方法
- [8.1.2 Adaboost 算法（P139）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [8.2 AdaBoost 算法的训练误差分析（P142-P145）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [8.3.2 前向分步算法与 AdaBoost（P145-P146）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [8.4.3 梯度提升（P151）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [8.1.3 AdaBoost 的例子（P140）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第九章 EM 算法及其推广
- [9.2 EM 算法的收敛性（P161）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [9.3.1 高斯混合模型（P163）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [9.4 EM 算法的推广（P167）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [9.3.1 高斯混合模型的 EM 算法（165）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 第十章隐马尔可夫模型
- [10.2.2 前向算法（P175-P176）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.2.3 后向算法（P178-P179）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.4.2 维特比算法（P185）]()
- [10.2.4 一些概率与期望值的计算（P179-P180）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.2.2 前向算法（P175-P177）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.2.3 后向算法（P178）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.2.4 一些概率与期望值的计算（P179-P178）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.3.1 监督学习方法（P180）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.3.2 Baum-Welch 算法（P181-P184）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.4.1 近似算法（P184）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [10.4.2 维特比算法（P185-P186）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- 十一章条件随机场
- 参考文献
- [附录 1 例 1.1 的 R 实现/训练误差与预测误差的对比](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 2 线性可分/不可分感知机的 R 实现 ](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 3 离散特征的 2 维平衡 kd 树 R 代码 ](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 4 离散特征的朴素贝叶斯法 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 5 决策树的实现的 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 6 逻辑斯蒂回归及最大熵模型的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 7 基于 SMO 算法的支持向量机的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 8 提升算法的 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 9 EM 算法的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)
- [附录 10 HMM 模型的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)

## ©️License

GNU General Public License v3.0

## 🎉Contribute

🌟如果觉得有用，不妨给个🌟👆🏻Star支持一下吧~