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: | 0.2.2 |
| Imports: | Rcpp (≥ 0.12.13), mvtnorm |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, knitr, rmarkdown, ggplot2, gganimate, gifski |
| Published: | 2025-09-20 |
| DOI: | 10.32614/CRAN.package.SSOSVM |
| Author: | Andrew Thomas Jones [aut, cre], Hien Duy Nguyen [aut], Geoffrey J. McLachlan [aut] |
| Maintainer: | Andrew Thomas Jones <andrewthomasjones at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README, NEWS |
| CRAN checks: | SSOSVM results |
| Reference manual: | SSOSVM.html , SSOSVM.pdf |
| Package source: | SSOSVM_0.2.2.tar.gz |
| Windows binaries: | r-devel: SSOSVM_0.2.2.zip, r-release: SSOSVM_0.2.2.zip, r-oldrel: SSOSVM_0.2.2.zip |
| macOS binaries: | r-release (arm64): SSOSVM_0.2.2.tgz, r-oldrel (arm64): SSOSVM_0.2.2.tgz, r-release (x86_64): SSOSVM_0.2.2.tgz, r-oldrel (x86_64): SSOSVM_0.2.2.tgz |
| Old sources: | SSOSVM archive |
Please use the canonical form https://CRAN.R-project.org/package=SSOSVM to link to this page.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.