SSOSVM (0.2.1)

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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). This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.

Maintainer: Andrew Thomas Jones
Author(s): Andrew Thomas Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

License: GPL-3

Uses: MASS, mvtnorm, Rcpp, ggplot2, testthat, knitr, rmarkdown, gifski, gganimate

Released 9 months ago.



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