Kernel-Based Regularized Least Squares
Description
Implements Kernel-based Regularized Least Squares (KRLS), a
machine learning method to fit multidimensional functions y = f(x) for
regression and classification problems without relying on linearity or
additivity assumptions. KRLS finds the best fitting function by
minimizing the squared loss of a Tikhonov regularization problem,
using Gaussian kernels as radial basis functions. For further details
see Hainmueller and Hazlett (2014, ).