Package: SSOSVM 0.2.2
Andrew Thomas Jones
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_0.2.2.tar.gz
SSOSVM_0.2.2.tar.gz(r-4.7-arm64)SSOSVM_0.2.2.tar.gz(r-4.7-x86_64)SSOSVM_0.2.2.tar.gz(r-4.6-arm64)SSOSVM_0.2.2.tar.gz(r-4.6-x86_64)
SSOSVM_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SSOSVM/json (API)
NEWS
| # Install 'SSOSVM' in R: |
| install.packages('SSOSVM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:ac3e40ba98. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 166 | ||
| linux-devel-x86_64 | OK | 131 | ||
| source / vignettes | OK | 276 | ||
| linux-release-arm64 | OK | 168 | ||
| linux-release-x86_64 | OK | 129 | ||
| wasm-release | OK | 139 |
Exports:generateSimHingeLogisticSquareHingeSVMFit
Dependencies:mvtnormRcppRcppArmadillo
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 |
