R package KRLS: Kernel-Based Regularized Least Squares. Package 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).
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References in zbMATH (referenced in 1 article )
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- Jeremy Ferwerda and Jens Hainmueller and Chad Hazlett: Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls) (2017) not zbMATH