https://github.com/alixunxing/lihang

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

https://github.com/alixunxing/lihang

Repository

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

https://github.com/alixunxing/Lihang/blob/master/

# 
![Hits](https://www.smirkcao.info/hit_gits/Lihang/README.md)

[![Gitter chat](https://badges.gitter.im/SmirkCao/StatisticalLearningMethods.png)](https://gitter.im/StatisticalLearningMethods/Book)[![Python](https://img.shields.io/badge/python-3.5|3.6|3.7-blue.svg)](-)[![pull](https://img.shields.io/badge/contributions-welcome-blue.svg)](https://github.com/SmirkCao/Lihang/pulls)

20195

[Release first_edition](https://github.com/SmirkCao/Lihang/archive/first_edition.zip)

[TOC]

## 



- GitHubmarkdownChrome[TeX All the Things](https://chrome.google.com/webstore/detail/tex-all-the-things/cbimabofgmfdkicghcadidpemeenbffn)TeX,Markdown[Typora](https://typora.io/)Ctrl+, PreferencesSyntax Supportinline MathUbuntuWindows
- math_markdown.pdf[math_markdown.md](./math_markdown.md)  markdown$\LaTeX$
- [ref_downloader](./ref_downloader.sh) 
- [glossary_index](./glossary_index.md) 
- [symbol_index](./symbol_index.md) 
- [errata_se](./errata_se.md) errata

## 

- 20195
- 513...
- 
- repo[](symbol_index.md)
- Apriori

Repo:

 `SmirkCao, Lihang, (2018), GitHub repository, https://github.com/SmirkCao/Lihang`



```
@misc{SmirkCao,
  author = {SmirkCao},
  title = {Lihang},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/SmirkCao/Lihang}},
  commit = {c5624a9bd757a5cc88e78b85b89e9221deb08270}
}
```



## 

****:

>1. ****.
>1. 
>1. 



>  

- 
- 



""

Refs[Refs/README.md](Refs/README.md)

 review02[ref_downloader.sh](ref_downloader.sh)review02

~~~~

Repoglossary_index.mdreview



### 

 PRML 1.6



### 

![ ](assets/content_distribution.png)

SVMMCMCDTHMMCRFSVDPCALDAPageRank

NBLRDTAdaBoostPerceptronSVMHMMCRF



## CH01 

[Introduction](CH01/README.md)

:

- 

- 

- 

  

  

## CH02 

[Perceptron](CH02/README.md)
- 
- .
## CH03 k

[kNN](CH03/README.md)
- kNN
- k, kNN.
## CH04 

[NB](CH04/README.md)
- .
1. $IID\rightarrow$
1. $Bayes\rightarrow$
- x, 0, 
  $$P_\lambda(X^{(j)}=a_{jl}|Y=c_k)=\frac{\sum_{i=1}^{N}{I(x_i^{(j)}=a_{jl}, y_i=c_k)}+\lambda}{\sum_{i=1}^{N}{I(y_i=c_k)+S_j\lambda}}$$
  - $\lambda = 0$ 
  - $\lambda = 1$ 
- , .

## CH05 

[DT](CH05/README.md)

- 
## CH06 

[LR](CH06/README.md)

- 
- , 

[1][Berger, 1996](Refs/README.md), 

, **LRMaxent?**
- 

- 

- , . , IIS, GD, BFGS

- [Logistic regression](http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression),

  > Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a [logistic function](https://en.wikipedia.org/wiki/Logistic_function).

- [](https://www.csie.ntu.edu.tw/~cjlin/papers/maxent_journal.pdf)

  >Logistic regression is a special case of maximum entropy with two labels +1 and 1.

  $y\in \mathcal{Y}=\{0,1\}$

- NLPMaxent

## CH07 

[SVM](CH07/README.md)

- 
- , [](CH02/README.md)
- margin

## CH08 

[Boosting](CH08/README.md)

- , .

## --------

HMMCRF****HMMMRFCRFHMMCRF

  

## CH09 EM

[EM](CH09/README.md)

- EM****(****)

- >  

  [CH04](CH04/README.md)

- BMMGMM

- EMEMEMHintonHinton2018ICLRCapsule NetworkMatrix Capsules with EM Routing

- CH22EM

## CH10 

[HMM](CH10/README.md)

- 
- 
- ****(Tagging)
- 

## CH11 

[CRF](CH11/README.md)

- ****
- ****
- 

## CH12 

[Summary](CH12/README.md)


- 

- 

- 

- 12.2$y$$\cal{Y}=\{+1,-1\}$[LR](CH06/README.md)$y$$\cal{Y}=\{0,1\}$



## --------

PageRank

## CH13 

[Introduction](./CH13/README.md)

- 
- ********
- 
- **** 

## CH14 

[Clustering](./CH14/README.md)

- 14.2
- 

## CH15 

- 
- 
- $U,V$
- 

## CH16 

- ****
- 01
-  
- ****LSAPLSALDAMCMCLDA
- 

## CH17 

- sklearnLSA
- LSAPCA
- LSA$U$DOC$SV^\mathrm{T}$sklaernxtransformed$U\mit\Sigma$ 

## CH18 

## CH19 

## CH20 

## CH21 PageRank

## CH22 

## 



![data_algo_map](assets/data_algo_map.png)



## 

[^1]: [Matrix Capsules with EM Routing](http://arxiv.org/abs/1710.09829)

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