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Core
SVMs and the Kernel Trick
A support vector machine finds the decision boundary with the widest margin to the nearest points (the support vectors), trading hinge loss against margin width. The kernel trick lets it draw nonlinear boundaries by computing inner products in a high-dimensional space without ever materializing the features. Interviews probe SVMs because they reward understanding margins, duality, and the specific regime (small, high-dimensional data) where they still beat trees and neural nets.
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