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.
|Date of publication||2016-05-14 23:12:09|
|Maintainer||Junxiang Wang <email@example.com>|
|License||GPL (>= 3)|
sslGmmEM: Gaussian Mixture Model with an EM Algorithm
sslLabelProp: Label Propagation
sslLapRLS: Laplacian Regularized Least Squares
sslLDS: Low Density Separation
sslLLGC: Local and Global Consistency
sslMarkovRandomWalks: t-step Markov Random Walks
sslRegress: Regression on graphs
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