Package: SVMMaj 0.2.9.3
SVMMaj: Implementation of the SVM-Maj Algorithm
Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.
Authors:
SVMMaj_0.2.9.3.tar.gz
SVMMaj_0.2.9.3.tar.gz(r-4.5-noble)SVMMaj_0.2.9.3.tar.gz(r-4.4-noble)
SVMMaj_0.2.9.3.tgz(r-4.4-emscripten)SVMMaj_0.2.9.3.tgz(r-4.3-emscripten)
SVMMaj.pdf |SVMMaj.html✨
SVMMaj/json (API)
# Install 'SVMMaj' in R: |
install.packages('SVMMaj', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- AusCredit - Australian Credit Approval Dataset
- AusCredit.te - Australian Credit Approval Dataset
- AusCredit.tr - Australian Credit Approval Dataset
- diabetes - Pima Indians Diabetes Data Set
- diabetes.te - Pima Indians Diabetes Data Set
- diabetes.tr - Pima Indians Diabetes Data Set
- supermarket1996 - Supermarket data 1996
- voting - Congressional Voting Records Data Set
- voting.te - Congressional Voting Records Data Set
- voting.tr - Congressional Voting Records Data Set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 days agofrom:54342280e7. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 23 2024 |
R-4.5-linux | OK | Nov 23 2024 |
Exports:aucclassificationgetHingeisbnormalizeplotWeightsroccurvesvmmajsvmmajcrossval
Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegridExtragtableisobandkernlablabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppreshape2rlangscalesstringistringrtibbletidyselectutf8vctrsviridisLitewithr