gelnet (version 1.2.1)

gelnet.klr: Kernel logistic regression

Description

Learns a kernel logistic regression model for a binary classification task

Usage

gelnet.klr(K, y, lambda, max.iter = 100, eps = 1e-05, v.init = rep(0,
  nrow(K)), b.init = 0.5, silent = FALSE)

Arguments

K

n-by-n matrix of pairwise kernel values over a set of n samples

y

n-by-1 vector of binary response labels

lambda

scalar, regularization parameter

max.iter

maximum number of iterations

eps

convergence precision

v.init

initial parameter estimate for the kernel weights

b.init

initial parameter estimate for the bias term

silent

set to TRUE to suppress run-time output to stdout (default: FALSE)

Value

• A list with two elements: [object Object],[object Object]

Details

The method operates by constructing iteratively re-weighted least squares approximations of the log-likelihood loss function and then calling the kernel ridge regression routine to solve those approximations. The least squares approximations are obtained via the Taylor series expansion about the current parameter estimates.

See Also

gelnet.krr, gelnet.logreg