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 gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
SSOSVM results |
Documentation:
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