Support Vector Regression: Nu-SVR and RVR

This lecture covers advanced topics in machine learning, focusing on Support Vector Regression (SVR) extensions. It explains the principles of SVR, hyperparameters optimization, and introduces Nu-SVR and Relevance Vector Regression (RVR). The instructor discusses the constraints optimization problem, hyperparameters C and E, and the precision control of the fit. The lecture also delves into the differences between E-SVR and V-SVR, the effect of automatic adaptation of ε, and the Bayesian approach in RVR. It concludes with a comparison of ε-SVR, V-SVR, and RVR using an RBF kernel, highlighting the performance and sparsity of each model.