kerndwd-package: Kernel Distance Weighted Discrimination
Description
Extremely novel efficient procedures for solving linear DWD and kernel DWD in reproducing kernel Hilbert spaces for classification. The algorithm is based on the majorization-minimization (MM) principle to compute the entire solution path at a given fine grid of regularization parameters.Details
Suppose x is the predictors and y is the binary response. The package computes the entire solution path over a grid of lambda values.
The main functions of the package kerndwd include:
kerndwd
cv.kerndwd
predict.kerndwd
plot.kerndwd
plot.cv.kerndwdReferences
Wang, B. and Zou, H. (2015)
``Another Look at DWD: Thrifty Algorithm and Bayes Risk Consistency in RKHS".
http://arxiv.org/abs/1508.05913v1.pdf
Karatzoglou, A., Smola, A., Hornik, K., and Zeileis, A. (2004)
``kernlab -- An S4 Package for Kernel Methods in R",
Journal of Statistical Software, 11(9), 1--20.
http://www.jstatsoft.org/v11/i09/paper
Marron, J.S., Todd, M.J., Ahn, J. (2007)
``Distance-Weighted Discrimination"",
Journal of the American Statistical Association, 102(408), 1267--1271.
https://faculty.franklin.uga.edu/jyahn/sites/faculty.franklin.uga.edu.jyahn/files/DWD3.pdf