Extremely novel efficient procedures for solving linear generalized DWD and kernel generalized 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.
Suppose x is predictor and y is a 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
tunedwd
predict.kerndwd
plot.kerndwd
plot.cv.kerndwd
Wang, B. and Zou, H. (2018) ``Another Look at Distance Weighted Discrimination," Journal of Royal Statistical Society, Series B, 80(1), 177--198. https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12244 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. https://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://www.tandfonline.com/doi/abs/10.1198/016214507000001120