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mabelc
ssc:Semi-Supervised Classification Methods
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.
Maintained by Christoph Bergmeir. Last updated 5 years ago.
9 stars 5.22 score 62 scripts 1 dependents
cran
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.
Maintained by Francisco Jesús Palomares Alabarce. Last updated 4 years ago.
1 stars 2.78 score