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ssc

Semi-Supervised Classification Methods

v2.1-0 · Dec 15, 2019 · GPL (>= 3)

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.

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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies proxy ssc

Version History

new 2.1-0 Mar 10, 2026
updated 2.1-0 ← 2.0.0 diff Dec 14, 2019
updated 2.0.0 ← 1.0 diff Mar 26, 2018
new 1.0 Oct 4, 2016