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ssc  

Semi-Supervised Classification Methods
View on CRAN: Click here


Download and install ssc package within the R console
Install from CRAN:
install.packages("ssc")

Install from Github:
library("remotes")
install_github("cran/ssc")

Install by package version:
library("remotes")
install_version("ssc", "2.1-0")



Attach the package and use:
library("ssc")
Maintained by
Christoph Bergmeir
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-10-05
Latest Update: 2019-12-15
Description:
Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.
How to cite:
Christoph Bergmeir (2016). ssc: Semi-Supervised Classification Methods. R package version 2.1-0, https://cran.r-project.org/web/packages/ssc. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0 (2016-10-05 09:30), 2.0.0 (2018-03-27 06:00)
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