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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.
Package details |
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Author | Junxiang Wang |
Maintainer | Junxiang Wang <xianggebenben@163.com> |
License | GPL (>= 3) |
Version | 0.1 |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
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