SSOSVM: Stream Suitable Online Support Vector Machines
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 |
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