5 packages on CRAN
This is a simple wrapper for the LASVM Solver (see http://leon.bottou.org/projects/lasvm). LASVM is basically an online variant of the SMO solver.
Solving the linear SVM problem with coordinate descent is very efficient and is implemented in one of the most often used packages, 'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear). It has been shown that the uniform selection of coordinates can be accelerated by using an online adaptation of coordinate frequencies (ACF). This package implements ACF and is based on 'LIBLINEAR' as well as the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>). It currently supports L2-regularized L1-loss as well as L2-loss linear SVM. Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and Crammer & Singer method) and cross validation for model selection is supported. The training of the models based on ACF is much faster than standard 'LIBLINEAR' on many problems.
An R interface to the C++/Boost Shark machine learning library.
This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means.
Three ensemble learning algorithms based on support vector machines. They all train support vector machines on subset of data and combine the result.