Semi-Supervised Learning

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.


Reference manual

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0.1 by Junxiang Wang, 3 years ago

Browse source code at

Authors: Junxiang Wang

Documentation:   PDF Manual  

GPL (>= 3) license

Imports NetPreProc, Rcpp, caret, proxy, xgboost, klaR, e1071, stats

Linking to Rcpp

See at CRAN