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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.

Functions (109)

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1
GPL-3
Authors
Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)
Initial release

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