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SSL (version 0.1)

Semi-Supervised Learning

Description

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.

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Version

Install

install.packages('SSL')

Version

0.1

License

GPL (>= 3)

Maintainer

Last Published

May 14th, 2016

Functions in SSL (0.1)

sslLabelProp

Label Propagation
sslMincut

Mincut
sslMarkovRandomWalks

t-step Markov Random Walks
sslGmmEM

Gaussian Mixture Model with an EM Algorithm
sslLapRLS

Laplacian Regularized Least Squares
sslRegress

Regression on graphs
sslCoTrain

Co-Training
sslSelfTrain

Self-Training
sslLLGC

Local and Global Consistency
sslLDS

Low Density Separation