KRLS: Kernel-based Regularized Least squares (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).
- Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
- Date of publication
- 2014-05-21 21:21:47
- Jens Hainmueller <email@example.com>
- GPL (>= 2)
- Compute first differences with KRLS
- Gaussian Kernel Distance Computation
- Kernel-based Regularized Least Squares (KRLS)
- Leave-one-out optimization to find lambda
- Loss Function for Leave One Out Error
- Plot method for Kernel-based Regularized Least Squares (KRLS)...
- Predict method for Kernel-based Regularized Least Squares...
- Solve for Choice Coefficients in KRLS
- Summary method for Kernel-based Regularized Least Squares...
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