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: 0.2.1
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>
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:, r-release:, r-oldrel:
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


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