EPISODE · Dec 4, 2017 · 14 MIN
Maximal Margin Classifiers
from Linear Digressions · host 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!
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