BaseClassifier          Classifier used for enabling shared documenting
                        of parameters
CrossValidationSSL      Cross-validation in semi-supervised setting
EMLeastSquaresClassifier
                        An Expectation Maximization like approach to
                        Semi-Supervised Least Squares Classification
EMLinearDiscriminantClassifier
                        Semi-Supervised Linear Discriminant Analysis
                        using Expectation Maximization
EMNearestMeanClassifier
                        Semi-Supervised Nearest Mean Classifier using
                        Expectation Maximization
EntropyRegularizedLogisticRegression
                        Entropy Regularized Logistic Regression
GRFClassifier           Label propagation using Gaussian Random Fields
                        and Harmonic functions
ICLeastSquaresClassifier
                        Implicitly Constrained Least Squares Classifier
ICLinearDiscriminantClassifier
                        Implicitly Constrained Semi-supervised Linear
                        Discriminant Classifier
KernelICLeastSquaresClassifier
                        Kernelized Implicitly Constrained Least Squares
                        Classification
KernelLeastSquaresClassifier
                        Kernelized Least Squares Classifier
LaplacianKernelLeastSquaresClassifier
                        Laplacian Regularized Least Squares Classifier
LaplacianSVM            Laplacian SVM classifier
LearningCurveSSL        Compute Semi-Supervised Learning Curve
LeastSquaresClassifier
                        Least Squares Classifier
LinearDiscriminantClassifier
                        Linear Discriminant Classifier
LinearSVM               Linear SVM Classifier
LinearSVM-class         LinearSVM Class
LinearTSVM              Linear CCCP Transductive SVM classifier
LogisticLossClassifier
                        Logistic Loss Classifier
LogisticLossClassifier-class
                        LogisticLossClassifier
LogisticRegression      (Regularized) Logistic Regression
                        implementation
LogisticRegressionFast
                        Logistic Regression implementation that uses
                        R's glm
MCLinearDiscriminantClassifier
                        Moment Constrained Semi-supervised Linear
                        Discriminant Analysis.
MCNearestMeanClassifier
                        Moment Constrained Semi-supervised Nearest Mean
                        Classifier
MCPLDA                  Maximum Contrastive Pessimistic Likelihood
                        Estimation for Linear Discriminant Analysis
MajorityClassClassifier
                        Majority Class Classifier
NearestMeanClassifier   Nearest Mean Classifier
PreProcessing           Preprocess the input to a classification
                        function
PreProcessingPredict    Preprocess the input for a new set of test
                        objects for classifier
QuadraticDiscriminantClassifier
                        Quadratic Discriminant Classifier
S4VM                    Safe Semi-supervised Support Vector Machine
                        (S4VM)
S4VM-class              LinearSVM Class
SSLDataFrameToMatrices
                        Convert data.frame to matrices for
                        semi-supervised learners
SVM                     SVM Classifier
SelfLearning            Self-Learning approach to Semi-supervised
                        Learning
TSVM                    Transductive SVM classifier using the convex
                        concave procedure
USMLeastSquaresClassifier
                        Updated Second Moment Least Squares Classifier
USMLeastSquaresClassifier-class
                        USMLeastSquaresClassifier
WellSVM                 WellSVM for Semi-supervised Learning
WellSVM_SSL             Convex relaxation of S3VM by label generation
WellSVM_supervised      A degenerated version of WellSVM where the
                        labels are complete, that is, supervised
                        learning
add_missinglabels_mar   Throw out labels at random
adjacency_knn           Calculate knn adjacency matrix
c.CrossValidation       Merge result of cross-validation runs on single
                        datasets into a the same object
clapply                 Use mclapply conditional on not being in
                        RStudio
cov_ml                  Biased (maximum likelihood) estimate of the
                        covariance matrix
decisionvalues          Decision values returned by a classifier for a
                        set of objects
df_to_matrices          Convert data.frame with missing labels to
                        matrices
diabetes                diabetes data for unit testing
find_a_violated_label   Find a violated label
gaussian_kernel         calculated the gaussian kernel matrix
generate2ClassGaussian
                        Generate data from 2 Gaussian distributed
                        classes
generateABA             Generate data from 2 alternating classes
generateCrescentMoon    Generate Crescent Moon dataset
generateFourClusters    Generate Four Clusters dataset
generateParallelPlanes
                        Generate Parallel planes
generateSlicedCookie    Generate Sliced Cookie dataset
generateSpirals         Generate Intersecting Spirals
generateTwoCircles      Generate data from 2 circles
geom_classifier         Plot RSSL classifier boundary (deprecated)
geom_linearclassifier   Plot linear RSSL classifier boundary
harmonic_function       Direct R Translation of Xiaojin Zhu's Matlab
                        code to determine harmonic solution
line_coefficients       Loss of a classifier or regression function
localDescent            Local descent
logsumexp               Numerically more stable way to calculate log
                        sum exp
loss                    Loss of a classifier or regression function
losslogsum              LogsumLoss of a classifier or regression
                        function
losspart                Loss of a classifier or regression function
                        evaluated on partial labels
measure_accuracy        Performance measures used in classifier
                        evaluation
minimaxlda              Implements weighted likelihood estimation for
                        LDA
missing_labels          Access the true labels for the objects with
                        missing labels when they are stored as an
                        attribute in a data frame
plot.CrossValidation    Plot CrossValidation object
plot.LearningCurve      Plot LearningCurve object
posterior               Class Posteriors of a classifier
predict,scaleMatrix-method
                        Predict for matrix scaling inspired by stdize
                        from the PLS package
print.CrossValidation   Print CrossValidation object
print.LearningCurve     Print LearningCurve object
projection_simplex      Project an n-dim vector y to the simplex Dn
responsibilities        Responsibilities assigned to the unlabeled
                        objects
rssl-formatting         Show RSSL classifier
rssl-predict            Predict using RSSL classifier
sample_k_per_level      Sample k indices per levels from a factor
scaleMatrix             Matrix centering and scaling
solve_svm               SVM solve.QP implementation
split_dataset_ssl       Create Train, Test and Unlabeled Set
split_random            Randomly split dataset in multiple parts
stat_classifier         Plot RSSL classifier boundaries
stderror                Calculate the standard error of the mean from a
                        vector of numbers
summary.CrossValidation
                        Summary of Crossvalidation results
svdinv                  Inverse of a matrix using the singular value
                        decomposition
svdinvsqrtm             Taking the inverse of the square root of the
                        matrix using the singular value decomposition
svdsqrtm                Taking the square root of a matrix using the
                        singular value decomposition
svmlin                  svmlin implementation by Sindhwani & Keerthi
                        (2006)
svmlin_example          Test data from the svmlin implementation
svmproblem              Train SVM
testdata                Example semi-supervised problem
threshold               Refine the prediction to satisfy the balance
                        constraint
true_labels             Access the true labels when they are stored as
                        an attribute in a data frame
wdbc                    wdbc data for unit testing
wellsvm_direct          wellsvm implements the wellsvm algorithm as
                        shown in [1].
wlda                    Implements weighted likelihood estimation for
                        LDA
wlda_error              Measures the expected error of the LDA model
                        defined by m, p, and iW on the data set a,
                        where weights w are potentially taken into
                        account
wlda_loglik             Measures the expected log-likelihood of the LDA
                        model defined by m, p, and iW on the data set
                        a, where weights w are potentially taken into
                        account
