KernelICLeastSquaresClassifier {RSSL} | R Documentation |
Kernelized Implicitly Constrained Least Squares Classification
Description
A kernel version of the implicitly constrained least squares classifier, see ICLeastSquaresClassifier
.
Usage
KernelICLeastSquaresClassifier(X, y, X_u, lambda = 0,
kernel = vanilladot(), x_center = TRUE, scale = TRUE, y_scale = TRUE,
lambda_prior = 0, classprior = 0, method = "LBFGS",
projection = "semisupervised")
Arguments
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
lambda |
numeric; L2 regularization parameter |
kernel |
kernlab::kernel to use |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
y_scale |
logical; whether the target vector should be centered |
lambda_prior |
numeric; regularization parameter for the posterior deviation from the prior |
classprior |
The classprior used to compare the estimated responsibilities to |
method |
character; Estimation method. One of c("LBFGS") |
projection |
character; The projection used. One of c("supervised","semisupervised") |
[Package RSSL version 0.9.7 Index]