Support Vector Machines: Exercises Solutions

This lecture covers the solutions to exercises related to Support Vector Machines (SVM) in the context of Applied Machine Learning. It explains the necessary conditions for optimality in SVM, the classifier function, and the decision function for points. The lecture also discusses the impact of penalty factor C and kernel width on the separating line in SVM. Additionally, it analyzes how the boundary changes with new data points and outliers, emphasizing the role of support vectors. The solutions demonstrate the influence of parameters on the boundary shape and the number of support vectors in SVM.