Support Vector Machines: Hyperparameters and V-SVM

This lecture covers the hyperparameters of Support Vector Machines (SVM), including the penalty factor C and the introduction of a new variable p in V-SVM to control the margin error. It explains the impact of different choices of hyperparameters on the optimization function and the number of support vectors. Additionally, it introduces the Relevance Vector Machine (RVM) as a sparse classification technique.