Comparison Across Methods: GMR vs SVR

This lecture compares the Generalized Matrix Regression (GMR) with Support Vector Regression (SVR) in the context of machine learning. GMR predicts trends away from data points, while SVR computes a weighted combination of local predictors. The lecture discusses the similarities and differences between the two methods, highlighting that GMR can predict multi-dimensional outputs, unlike SVR. It also covers the hyperparameters of both techniques and concludes that there is no straightforward way to determine which regression technique fits best for a given problem.