Explain Kernel Trick in SVM.

Explain Kernel Trick in SVM.

Approach: Explain mapping data to higher dimensions without explicit computation.

A method that allows Support Vector Machines to find non-linear boundaries by implicitly mapping input data into high-dimensional feature spaces. It computes the inner products between the images of all pairs of data in the feature space using a kernel function.

Follow-up: Name a common kernel function.

Key concepts: feature mapping, dot product, hilbert space.