Support Vector Machines: Soft Margin SVM

This lecture covers the concept of Soft Margin SVM, which aims to find a compromise between classification errors and the margin width in cases where data are not linearly separable. It introduces the idea of minimizing the inverse of the square of the margin to provide flexibility to the margin. The lecture also discusses the dual formulation of the Soft Margin SVM problem and the decision function derived from it.