Support Vector Regression: Kernels and Sparse Solutions for Regression | NTU Hsuan-Tien Lin Machine Learning Techniques Lecture 6
The representer theorem turns ridge regression into a problem in β, which gives kernel ridge regression the analytic solution β = (λI + K)⁻¹y, but almost none of the N values βₙ are 0. Replace the squared error with the ε-insensitive tube error, write the problem as a QP along the soft-margin SVM route, take the dual, and you get support vector regression (SVR): the dual has 2N variables, and every point strictly inside the tube has βₙ equal to 0.