KRLS (1.0-0)

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Kernel-Based Regularized Least Squares.

https://www.r-project.org
https://www.stanford.edu/~jhain/
http://cran.r-project.org/web/packages/KRLS

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).

Maintainer: Jens Hainmueller
Author(s): Jens Hainmueller (Stanford) Chad Hazlett (UCLA)

License: GPL (>= 2)

Uses: lattice
Reverse suggests: caret, fscaret

Released over 2 years ago.


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