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).
- Version1.0-0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- KRLS citation info
- Last release07/10/2017
Team
Jens Hainmueller
Chad Hazlett
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