Support Vector Machines
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Find the hyperplane that maximally separates classes. The kernel trick enables nonlinear decision boundaries by mapping to higher-dimensional spaces.
Key Properties
How It Works
SVM finds the decision boundary with the largest margin between classes. Support vectors are the data points closest to the boundary. Kernels (RBF, polynomial) enable nonlinear separation.
Related
- Linear Models (SVM with linear kernel)
- Classification Metrics (evaluate SVM performance)