Semi-Supervised Classification, Regression and Clustering Methods

Providing a collection of techniques for semi-supervised classification, regression and clustering. 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.


Reference manual

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install.packages("SSLR") by Francisco Jesús Palomares Alabarce, 3 months ago

Browse source code at

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, conclust

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

Linking to Rcpp, RcppArmadillo

See at CRAN