Semi-Supervised Classification, Regression and Clustering Methods
Best Split function
An S4 method to best split
Function calculate gini
General Interface Pairwise Constrained Clustering By Local Search
Check value in leaf
Ceck interface x y
General Interface COP K-Means Algorithm
Cluster labels
Get labels of clusters
General Interface for CoBC model
Combining the hypothesis
CoBC generic method
General Interface coBCReg model
Generic Interface coBCReg model
General Interface Constrained KMeans
General Interface for COREG model
Class DecisionTreeClassifier
General Interface for Democratic model
Combining the hypothesis of the classifiers
Democratic generic method
General Interface for EMLeastSquaresClassifier model
General Interface for EMNearestMeanClassifier model
General Interface for EntropyRegularizedLogisticRegression model
Fit with formula and data
Fit decision tree
An S4 method to fit decision tree.
Fit Random Forest
Fit with x , y (labeled data) and unlabeled data (x_U)
fit_x_u object
Fit with x and y
Cluster labels
Get centers model of clustering
Get most frequented
Get mean probability over all trees as prob vector
FUNCTION TO GET FUNCTION METHOD
FUNCTION TO GET FUNCTION METHOD
Function to get gtoup from gini index
Get most frequented
Get value mean
FUNCTION TO GET REAL X AND Y WITH FORMULA AND DATA
Gini or Variance by column
Function to compute Gini index
General Interface for GRFClassifier (Label propagation using Gaussian ...
Function grow tree
An S4 method to grow tree.
knn_regression
General Interface for LaplacianSVM model
General LCVQE Algorithm
General Interface for LinearTSVM model
Load conclust
Load parsnip
Load parsnip
Load RSSL
General Interface for MCNearestMeanClassifier (Moment Constrained Semi...
General Interface MPC K-Means Algorithm
Function to create DecisionTree
Class Node for Decision Tree
An S4 class to represent a class with more types values: null, numeric...
1-NN supervised classifier builder
Function to predict inputs in Decision Tree
Function to predict inputs in Decision Tree
Predictions of the coBC method
Predictions of the COREG method
Predictions of the Democratic method
Predict EMLeastSquaresClassifierSSLR
Predict EMNearestMeanClassifierSSLR
Predict EntropyRegularizedLogisticRegressionSSLR
Predict LaplacianSVMSSLR
Predict LinearTSVMSSLR
Predict MCNearestMeanClassifierSSLR
Predictions of model_sslr_fitted class
Model Predictions
Predictions of the SSLRDecisionTree_fitted method
Predictions of the Self-training method
Predictions of the SETRED method
Predictions of the SNNRCE method
Predictions of the SNNRCE method
Predictions of the SSLRDecisionTree_fitted method
Predictions of the Tri-training method
Predict TSVMSSLR
Predict USMLeastSquaresClassifierSSLR
Predict WellSVMSSLR
Predict inputs Decision Tree
An S4 method to predict inputs.
predictions unlabeled data
Predictions of unlabeled data
predictions unlabeled data
Print model SSLR
Class Random Forest
Objects exported from other packages
General Interface Seeded KMeans
General Interface for Self-training model
Self-training generic method
General Interface for SETRED model
SETRED generic method
General Interface for SNNRCE model
General Interface Decision Tree model
General Interface Random Forest model
FUNCTION TO TRAIN GENERIC MODEL
General Interface for Tri-training model
Combining the hypothesis
Tri-training generic method
General Interface for TSVM (Transductive SVM classifier using the conv...
General Interface for USMLeastSquaresClassifier (Updated Second Moment...
General Interface for WellSVM model
Providing a collection of techniques for semi-supervised classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled data are used to train a classifier. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface that is easy to use.