svm-dual-enables-kernel-trick

IN premise — entries/2026/06/21/wiki-Support_vector_machine-chunk-3.md

Created 2026-06-21T09:55:54+00:00

The dual formulation of the SVM involves only dot products between data points (x_i^T x_j), which is what enables the kernel trick by replacing these with k(x_i, x_j).

Dependents

These beliefs depend on this one:

• IN svm-mathematical-elegance-convex-kernel-dual — SVMs achieve mathematical elegance through three interlocking properties: the convex objective guarantees global optimality, the dual formulation exposes dot products, and the kernel trick maps those dot products into high-dimensional spaces without explicit computation.