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).
|Author||Jens Hainmueller (Stanford) Chad Hazlett (UCLA) Luke Sonnet (UCLA)|
|Maintainer||Jens Hainmueller <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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