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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.2 |
rolling linux/jammy R-4.5 | SSOSVM_0.2.2.tar.gz |
486.5 KiB |
0.2.2 |
rolling linux/noble R-4.5 | SSOSVM_0.2.2.tar.gz |
489.2 KiB |
0.2.2 |
rolling source/ R- | SSOSVM_0.2.2.tar.gz |
408.4 KiB |
0.2.2 |
latest linux/jammy R-4.5 | SSOSVM_0.2.2.tar.gz |
486.5 KiB |
0.2.2 |
latest linux/noble R-4.5 | SSOSVM_0.2.2.tar.gz |
489.2 KiB |
0.2.2 |
latest source/ R- | SSOSVM_0.2.2.tar.gz |
408.4 KiB |
0.2.2 |
2026-04-26 source/ R- | SSOSVM_0.2.2.tar.gz |
408.4 KiB |
0.2.2 |
2026-04-23 source/ R- | SSOSVM_0.2.2.tar.gz |
408.4 KiB |
0.2.2 |
2026-04-09 windows/windows R-4.5 | SSOSVM_0.2.2.zip |
807.6 KiB |
0.2.1 |
2025-04-20 source/ R- | SSOSVM_0.2.1.tar.gz |
406.4 KiB |