SSLR: 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.

Package details

AuthorFrancisco Jesús Palomares Alabarce [aut, cre] (<>), José Manuel Benítez [ctb] (<>), Isaac Triguero [ctb] (<>), Christoph Bergmeir [ctb] (<>), Mabel González [ctb] (<>)
MaintainerFrancisco Jesús Palomares Alabarce <>
Package repositoryView on CRAN
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SSLR documentation built on July 22, 2021, 9:08 a.m.