Support Vector Regression: Recap and Convex Optimization

This lecture covers the recap of Support Vector Regression (SVR) with a focus on the primal and dual formulations, stationary conditions, and the concept of convex-quadratic programming. It also delves into the duality of SVR, the primal Lagrangian, and the duality gap. The instructor explains the duality between SVR and Gaussian Process Regression (GPR), showcasing how SVR can be equivalent to GPR under certain conditions.