Package: SSOSVM 0.2.2

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.

Authors:Andrew Thomas Jones [aut, cre], Hien Duy Nguyen [aut], Geoffrey J. McLachlan [aut]