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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.
Getting started
Browse package contents
Package details |
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| Author | 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>) |
| Maintainer | Francisco Jesús Palomares Alabarce <fpalomares@correo.ugr.es> |
| License | GPL-3 |
| Version | 0.9.3.3 |
| URL | https://dicits.ugr.es/software/SSLR/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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Try the SSLR package in your browser
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