List of models"

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

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SSLR contains models created by developers and wrappers of different packages such as RSSL. From RSSL, we use S3VM methods.

The list of models is:

• Classification: SelfTraining(),SSLRDecisionTree(), SSLRRandomForest(), triTraining(), coBC(), democratic(), EMLeastSquaresClassifierSSLR(), EMNearestMeanClassifierSSLR(), EntropyRegularizedLogisticRegressionSSLR(), LaplacianSVMSSLR(), LinearTSVMSSLR(), WellSVMSSLR(), MCNearestMeanClassifierSSLR(), oneNN(), setred(), snnrce(), TSVMSSLR(), USMLeastSquaresClassifierSSLR(), GRFClassifierSSLR()

• Regression: coBC(),COREG(), SSLRDecisionTree(), SSLRRandomForest()

• Clustering: constrained_kmeans(), seeded_kmeans(), ckmeansSSLR(), cclsSSLR(), mpckmSSLR(), lcvqeSSLR()

NOTE: In the Regression modelling section we can see more examples of use in regression tasks. In Decision Tree , Random Forest and coBC we only have examples for classification tasks.

NOTE: In the Clustering modelling section we can see how to plot clusters with factoextra package.

SSLR documentation built on July 22, 2021, 9:08 a.m.