Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.

Documentation

Manual: SSL.pdf
Vignette: None available.

Maintainer: Junxiang Wang <xianggebenben at 163.com>

Author(s): Junxiang Wang

Install package and any missing dependencies by running this line in your R console:

install.packages("SSL")

Depends R (>= 3.2)
Imports NetPreProc(>=1.1), Rcpp(>=0.12.2), caret(>=6.0-52), proxy(>=0.4-15), xgboost(>=0.4), klaR(>=0.6-12), e1071(>=1.6-7), stats (>= 3.2)
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Package SSL
Materials
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Version 0.1
Published 2016-05-14
License GPL (>= 3)
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks SSL check results
Package source SSL_0.1.tar.gz