Package: ssc 2.1-0
Christoph Bergmeir
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.
Authors:
ssc_2.1-0.tar.gz
ssc_2.1-0.zip(r-4.5)ssc_2.1-0.zip(r-4.4)ssc_2.1-0.zip(r-4.3)
ssc_2.1-0.tgz(r-4.4-any)ssc_2.1-0.tgz(r-4.3-any)
ssc_2.1-0.tar.gz(r-4.5-noble)ssc_2.1-0.tar.gz(r-4.4-noble)
ssc_2.1-0.tgz(r-4.4-emscripten)ssc_2.1-0.tgz(r-4.3-emscripten)
ssc.pdf |ssc.html✨
ssc/json (API)
# Install 'ssc' in R: |
install.packages('ssc', repos = c('https://mabelc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mabelc/ssc/issues
Last updated 5 years agofrom:4565f07e0f. Checks:ERROR: 1 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | FAIL | Nov 17 2024 |
R-4.5-win | WARNING | Nov 17 2024 |
R-4.5-linux | WARNING | Nov 17 2024 |
R-4.4-win | WARNING | Nov 17 2024 |
R-4.4-mac | WARNING | Nov 17 2024 |
R-4.3-win | WARNING | Nov 17 2024 |
R-4.3-mac | WARNING | Nov 17 2024 |
Exports:coBCcoBCCombinecoBCGdemocraticdemocraticCombinedemocraticGoneNNselfTrainingselfTrainingGsetredsetredGsnnrcetriTrainingtriTrainingCombinetriTrainingG
Dependencies:proxy
Readme and manuals
Help Manual
Help page | Topics |
---|---|
CoBC method | coBC |
Combining the hypothesis | coBCCombine |
CoBC generic method | coBCG |
Time series data set | coffee |
Democratic method | democratic |
Combining the hypothesis of the classifiers | democraticCombine |
Democratic generic method | democraticG |
1-NN supervised classifier builder | oneNN |
Predictions of the coBC method | predict.coBC |
Predictions of the Democratic method | predict.democratic |
Model Predictions | predict.OneNN |
Predictions of the Self-training method | predict.selfTraining |
Predictions of the SETRED method | predict.setred |
Predictions of the SNNRCE method | predict.snnrce |
Predictions of the Tri-training method | predict.triTraining |
Self-training method | selfTraining |
Self-training generic method | selfTrainingG |
SETRED method | setred |
SETRED generic method | setredG |
SNNRCE method | snnrce |
Tri-training method | triTraining |
Combining the hypothesis | triTrainingCombine |
Tri-training generic method | triTrainingG |
Wine recognition data | wine |