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.