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.

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Package details

AuthorAndrew Thomas Jones, Hien Duy Nguyen, Geoffrey J. McLachlan
MaintainerAndrew Thomas Jones <>
Package repositoryView on CRAN
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SSOSVM documentation built on May 6, 2019, 5:01 p.m.