Package: SSOSVM 0.2.1
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:
SSOSVM_0.2.1.tar.gz
SSOSVM_0.2.1.zip(r-4.5)SSOSVM_0.2.1.zip(r-4.4)SSOSVM_0.2.1.zip(r-4.3)
SSOSVM_0.2.1.tgz(r-4.4-x86_64)SSOSVM_0.2.1.tgz(r-4.4-arm64)SSOSVM_0.2.1.tgz(r-4.3-x86_64)SSOSVM_0.2.1.tgz(r-4.3-arm64)
SSOSVM_0.2.1.tar.gz(r-4.5-noble)SSOSVM_0.2.1.tar.gz(r-4.4-noble)
SSOSVM_0.2.1.tgz(r-4.4-emscripten)SSOSVM_0.2.1.tgz(r-4.3-emscripten)
SSOSVM.pdf |SSOSVM.html✨
SSOSVM/json (API)
| # Install 'SSOSVM' in R: |
| install.packages('SSOSVM', repos = c('https://andrewthomasjones.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andrewthomasjones/ssosvm/issues
Last updated 5 years agofrom:e553051b79. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 12 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 12 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 12 2024 |
| R-4.4-win-x86_64 | NOTE | Oct 12 2024 |
| R-4.4-mac-x86_64 | NOTE | Oct 12 2024 |
| R-4.4-mac-aarch64 | NOTE | Oct 12 2024 |
| R-4.3-win-x86_64 | NOTE | Oct 12 2024 |
| R-4.3-mac-x86_64 | NOTE | Oct 12 2024 |
| R-4.3-mac-aarch64 | NOTE | Oct 12 2024 |
Exports:generateSimHingeLogisticSquareHingeSVMFit
Dependencies:MASSmvtnormRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generate Simulations | generateSim |
| Hinge | Hinge |
| Logistic Loss Function | Logistic |
| Square Hinge | SquareHinge |
| SSOSVM: A package for online training of soft-margin support vector machines (SVMs) using the Stochastic majorization–minimization (SMM) algorithm. | SSOSVM-package SSOSVM |
| SSOSVM Fit function | SVMFit |
