朴素贝叶斯和SVM

 朴素贝叶斯决策:

详解最大似然估计（MLE）、最大后验概率估计（MAP），以及贝叶斯公式的理解_nebulaf91的博客-CSDN博客_最大后验估计

如何简单理解贝叶斯决策理论（Bayes Decision Theory）？ - 知乎

参数估计 | Python 与机器学习

如何通俗地理解概率论中的「极大似然估计法」? - 知乎

 SVM:

机器学习实战教程（八）：支持向量机原理篇之手撕线性SVM

支持向量机：Outliers

The following Python packages are
recommended:
•CVXOPT(https://cvxopt.org/)
•PyCVX (https://www.cvxpy.org/install/) 

Support Vector Machine: Python implementation using CVXOPT — Data Blog

https://github.com/monroyaume5/Soft-Margin-SVM-with-CVXOPT 

https://github.com/Sharut/SVM-hard_margin-using-CVXPY/blob/master/MySVM.ipynb

https://www.youtube.com/watch?v=gBTtR0bs-1k&ab_channel=AladdinPersson

 Support vector machine (SVM) — OSQP documentation

optimization tutorial

Fitting Support Vector Machines via Quadratic Programming

SVM Using CVXPY – Explore AI