RMKL: Multiple Kernel Learning for Classification or Regression Problems

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.

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.0), caret, kernlab, stats, e1071
LinkingTo: Rcpp, RcppArmadillo
Published: 2019-04-25
Author: Christopher Wilson, Kaiqiao Li
Maintainer: Christopher Wilson <cwilso6 at clemson.edu>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: RMKL results

Downloads:

Reference manual: RMKL.pdf
Package source: RMKL_1.0.tar.gz
Windows binaries: r-devel: RMKL_1.0.zip, r-release: RMKL_1.0.zip, r-oldrel: RMKL_1.0.zip
OS X binaries: r-release: RMKL_1.0.tgz, r-oldrel: RMKL_1.0.tgz

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