Maximal Margin Classifiers - a podcast by Ben Jaffe and Katie Malone

Maximal margin classifiers are a way of thinking about supervised learning entirely in terms of the decision boundary between two classes, and defining that boundary in a way that maximizes the distance from any given point to the boundary. It's a neat way to think about statistical learning and a prerequisite for understanding support vector machines, which we'll cover next week--stay tuned!

Further episodes of Linear Digressions

Further podcasts by Ben Jaffe and Katie Malone

Website of Ben Jaffe and Katie Malone