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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SSOSVM to link to this page.
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