The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| DOI: | 10.32614/CRAN.package.SSOSVM |
| 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 (arm64): SSOSVM_0.2.1.tgz, r-oldrel (arm64): SSOSVM_0.2.1.tgz, r-release (x86_64): SSOSVM_0.2.1.tgz, r-oldrel (x86_64): SSOSVM_0.2.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=SSOSVM to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.