Description Usage Arguments Value Author(s) See Also Examples
The Suport Vector Regression (SVR) employs epsilon-intensive loss which ignores errors smaller than epsilon. This algorithm computes the entire paths for SVR solution as a function of epsilon
at a given regularization parameter lambda
, which we call epsilon
path.
1 2 |
x |
The data matrix (n x p) with n rows (observations) on p variables (columns) |
y |
The real number valued response variable |
lambda |
The regularization parameter value. |
kernel.function |
User defined kernel function. See |
param.kernel |
Parameter(s) of the kernels. See |
ridge |
Sometimes the algorithm encounters singularities; in this case a small value of ridge can help, default is |
eps |
A small machine number which is used to identify minimal step sizes |
eps.min |
The smallest value of epsilon for termination of the algorithm. Default is |
... |
Generic compatibility |
An 'epspath' object is returned.
Do Hyun Kim, Seung Jun Shin
predict.epspath
, plot.epspath
, svrpath
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