KernelICLeastSquaresClassifier: Kernelized Implicitly Constrained Least Squares...

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

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")

RSSL documentation built on May 29, 2024, 2:38 a.m.