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.
Version: | 0.1 |
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) |
LinkingTo: | Rcpp |
Published: | 2016-05-14 |
Author: | Junxiang Wang |
Maintainer: | Junxiang Wang <xianggebenben at 163.com> |
BugReports: | NA |
License: | GPL (≥ 3) |
URL: | NA |
NeedsCompilation: | yes |
CRAN checks: | SSL results |
Reference manual: | SSL.pdf |
Package source: | SSL_0.1.tar.gz |
Windows binaries: | r-devel: SSL_0.1.zip, r-release: SSL_0.1.zip, r-oldrel: SSL_0.1.zip |
OS X Mavericks binaries: | r-release: SSL_0.1.tgz, r-oldrel: SSL_0.1.tgz |