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.