cwilso6/RMKL: Multiple Kernel Learning for Classification, Regression Problems, and Survival Settings

Provides R and C++ function that enable the user to conduct multiple kernel learning (MKL) and cross validation for support vector machine (SVM) models. Cross validation can be used to identify kernel shapes and hyperparameter combinations that can be used as candidate kernels for MKL. There are three implementations provided in this package, namely SimpleMKL Alain Rakotomamonjy et. al (2008), Simple and Efficient MKL Xu et. al (2010), and Dual augmented Lagrangian MKL Suzuki and Tomioka (2011) <doi:10.1007/s10994-011-5252-9>. These methods identify the convex combination of candidate kernels to construct an optimal hyperplane. We also have added implementation of MKCox developed in Fenchel duality of Cox partial likelihood and its application in survival kernel learning Wilson et. al (2020) <DOI: 10.1101/2020.05.04.077263>.

install.packages("remotes")
remotes::install_github("cwilso6/RMKL")