Semi-Supervised Classification and Regression Methods

Providing a collection of techniques for semi-supervised classification and regression. In semi-supervised problem, both labeled and unlabeled data are used to train a classifier. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface that is easy to use.


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Reference manual

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install.packages("SSLR")

0.9.1 by Francisco Jesús Palomares Alabarce, 3 months ago


https://dicits.ugr.es/software/SSLR/


Browse source code at https://github.com/cran/SSLR


Authors: Francisco Jesús Palomares Alabarce [aut, cre] , José Manuel Benítez [ctb] , Isaac Triguero [ctb] , Christoph Bergmeir [ctb] , Mabel González [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports stats, parsnip, plyr, dplyr, magrittr, purrr, rlang, proxy, methods, generics, utils, RANN, foreach, RSSL

Suggests caret, tidymodels, e1071, C50, kernlab, testthat, doParallel, tidyverse, survival, xgboost, covr, kknn, randomForest, ranger, MASS, nlme, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo


See at CRAN