Support Vector Machines

We stay in the setting of Chapter 13 , but now we want to find the “best” classifier for a linearly separable dataset. More formally, the support vector machine (SVM) is precisely that classifier for which the decision boundary has the largest possible distance to the data. We reduce the task of finding the SVM to a quadratic optimization problem using the Karush-Kuhn-Tucker theorem and then discuss interpretations of the Lagrange multipliers that emerge.