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:Andrew Thomas Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

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)
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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'))

Peer review:

Bug tracker:https://github.com/andrewthomasjones/ssosvm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

2.70 score 3 scripts 133 downloads 5 exports 4 dependencies

Last updated 6 years agofrom:e553051b79. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-win-x86_64NOTENov 11 2024
R-4.5-linux-x86_64NOTENov 11 2024
R-4.4-win-x86_64NOTENov 11 2024
R-4.4-mac-x86_64NOTENov 11 2024
R-4.4-mac-aarch64NOTENov 11 2024
R-4.3-win-x86_64NOTENov 11 2024
R-4.3-mac-x86_64NOTENov 11 2024
R-4.3-mac-aarch64NOTENov 11 2024

Exports:generateSimHingeLogisticSquareHingeSVMFit

Dependencies:MASSmvtnormRcppRcppArmadillo