Support vector regression can solve both linear and non-linear models. SVM uses non-linear kernel functions (such as polynomial) to find the optimal solution for non-linear models.
The main idea of SVR is to minimize error, individualizing the hyperplane which maximizes the margin.
- https://www.youtube.com/watch?v=efR1C6CvhmE
- https://www.kaggle.com/gxkok21/support-vector-regression-from-scratch
- https://github.com/adityajn105/SVM-From-Scratch
- https://aihubprojects.com/svm-from-scratch-python/
- https://stats.stackexchange.com/questions/82044/how-does-support-vector-regression-work-intuitively
- https://fordcombs.medium.com/svm-from-scratch-step-by-step-in-python-f1e2d5b9c5be
- https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/
- https://www.mygreatlearning.com/blog/support-vector-regression/