Package: SSOSVM
Type: Package
Title: Stream Suitable Online Support Vector Machines
Version: 0.2.1
Date: 2019-05-06
Author: Andrew Thomas Jones, Hien Duy Nguyen,  Geoffrey J. McLachlan
Maintainer: Andrew Thomas Jones <andrewthomasjones@gmail.com>
Description: 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.
License: GPL-3
Encoding: UTF-8
Imports: Rcpp (>= 0.12.13), mvtnorm, MASS
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, ggplot2, gganimate, gifski
NeedsCompilation: yes
Packaged: 2019-05-06 08:56:26 UTC; andrewjones
Repository: CRAN
Date/Publication: 2019-05-06 09:10:03 UTC
Built: R 4.6.0; aarch64-apple-darwin20; 2025-07-18 04:58:41 UTC; unix
Archs: SSOSVM.so.dSYM
