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.
|Author||Andrew Thomas Jones, Hien Duy Nguyen, Geoffrey J. McLachlan|
|Maintainer||Andrew Thomas Jones <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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