How does kernel SVM map data implicitly into high dimensions?

Kernel functions compute inner products in transformed space without explicit mapping.

Instead of explicitly transforming x into φ(x), kernel functions compute K(x_i, x_j)=φ(x_i)·φ(x_j). This allows high or infinite-dimensional transformations efficiently.