RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features

Mateus P. Otto, Rafael Izbicki

Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to…