Kernel Trick
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Implicitly maps data to a higher-dimensional space where it becomes linearly separable, without actually computing the transformation. Common kernels: RBF (Gaussian), polynomial, sigmoid. Enables SVMs to learn nonlinear decision boundaries efficiently.