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AI & Machine Learning · Intermediate

Support Vector Machine (SVM)

A supervised algorithm that finds the hyperplane separating classes with the maximum margin.

Definition

SVMs choose the decision boundary that maximizes distance to the nearest training points, called support vectors. Kernel functions such as RBF let SVMs model nonlinear boundaries by implicitly mapping data to higher dimensions. They work well on medium-sized, high-dimensional datasets but scale poorly to very large ones.

Exam tip

The kernel trick is the key SVM concept for nonlinear data.

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