Support Vector Regression: Principles and Optimization

This lecture introduces Support Vector Regression (SVR) as an extension of the support vector machine framework for classification to estimate continuous functions. It covers the linear case, the E-tube concept, the ε-margin, optimization problems, and the solution for both linear and non-linear regression. The lecture also delves into the interpretation of SVR solutions, the role of hyperparameters in SVR optimization, and their influence on the fit.