Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.
| Version: | 0.2.1 |
| Imports: | Rcpp (≥ 0.12.13), mvtnorm, MASS |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, knitr, rmarkdown, ggplot2, gganimate, gifski |
| Published: | 2019-05-06 |
| Author: | Andrew Thomas Jones, Hien Duy Nguyen, Geoffrey J. McLachlan |
| Maintainer: | Andrew Thomas Jones <andrewthomasjones at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | SSOSVM results |
| Reference manual: | SSOSVM.pdf |
| Package source: | SSOSVM_0.2.1.tar.gz |
| Windows binaries: | r-devel: SSOSVM_0.2.1.zip, r-release: SSOSVM_0.2.1.zip, r-oldrel: SSOSVM_0.2.1.zip |
| macOS binaries: | r-release: SSOSVM_0.2.1.tgz, r-oldrel: SSOSVM_0.2.1.tgz |
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