SVM
[PCA](https://texonom.com/pca-5f0f360d90944cccb62613910c3bf9f6) 


## **Support vector machine**
2000s was age of SVM powerful classifier before deep learning
No direct multi-class SVM, must combine two-class SVMs but good for small dataset
w is weight, b is bias margin $\gamma$
1. 입력 데이터를 선형 또는 비선형으로 분리할 수 있는 초기 결정 경계를 찾습니다.
2. 초기 결정 경계와 가장 가까운 입력 데이터들을 서포트 벡터로 선택합니다.
3. 서포트 벡터들을 사용하여 초기 결정 경계를 업데이트합니다.
4. 2~3단계를 반복하여 최적의 결정 경계를 찾습니다.
### SVM Notion
|Title|
|:-:|
|[Support Vector](https://texonom.com/support-vector-e18f7e6ff16a4ce08d7ce4c34e3106d3)|
|[Functional Margin](https://texonom.com/functional-margin-71a665c7f96a4fed8b51b6fa70baa0ad)|
|[Geometric Margin](https://texonom.com/geometric-margin-fdbba20e3faa430f95cfe3aa263d26db)|
|[Optimal Margin Classifier](https://texonom.com/optimal-margin-classifier-5151f4af7d884f3fb4c9e1de07a59f95)|
|[Maximum Margin Classifier](https://texonom.com/maximum-margin-classifier-9e209bbbf89d4ebdba08497821d97cf7)|
|[SMO](https://texonom.com/smo-54f83167822443f4b4922a6aad94b6df)|
|[Slack Variable](https://texonom.com/slack-variable-e91b24cd50c64e96a0cebf4c81d6f273)|
|[Soft-margin Clasffier](https://texonom.com/soft-margin-clasffier-92ccd972993f4c708a755891e8b9e0a7)|


### SVMs
|Title|
|:-:|
|[TSVM](https://texonom.com/tsvm-7ec2ffe8f06f4a82b5388d5192fc0119)|
|[Latent SVM](https://texonom.com/latent-svm-29b86dc7d0624c8fb1657e4ddae72e72)|
|[Structural SVM](https://texonom.com/structural-svm-c4800f5fa5634eb59c9149c23f777682)|



> [Untitled](https://www.linkedin.com/feed/update/urn:li:activity:7082025501048918016/)

### Implementations
> [Software
        —
        Kernel Machines](http://www.kernel-machines.org/software)

> [Support vector machine](https://en.wikipedia.org/wiki/Support_vector_machine)






