# Implementations of Semi-Supervised Learning Approaches for Classification

## Documentation for package ‘RSSL’ version 0.9.5

- DESCRIPTION file.

## Help Pages

A B C D E F G H I K L M N P Q R S T U W

## -- A --

add_missinglabels_mar | Throw out labels at random
adjacency_knn | Calculate knn adjacency matrix

## -- B --

BaseClassifier | Classifier used for enabling shared documenting of parameters

## -- C --

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
CrossValidationSSL | Cross-validation in semi-supervised setting
CrossValidationSSL.list | Cross-validation in semi-supervised setting
CrossValidationSSL.matrix | Cross-validation in semi-supervised setting

## -- D --

decisionvalues | Decision values returned by a classifier for a set of objects
decisionvalues-method | Decision values returned by a classifier for a set of objects
decisionvalues-method | Predict using RSSL classifier
df_to_matrices | Convert data.frame with missing labels to matrices
diabetes | diabetes data for unit testing

## -- E --

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

## -- F --

find_a_violated_label | Find a violated label

## -- G --

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
GRFClassifier | Label propagation using Gaussian Random Fields and Harmonic functions

## -- H --

harmonic_function | Direct R Translation of Xiaojin Zhu's Matlab code to determine harmonic solution

## -- I --

ICLeastSquaresClassifier | Implicitly Constrained Least Squares Classifier
ICLinearDiscriminantClassifier | Implicitly Constrained Semi-supervised Linear Discriminant Classifier

## -- K --

KernelICLeastSquaresClassifier | Kernelized Implicitly Constrained Least Squares Classification
KernelLeastSquaresClassifier | Kernelized Least Squares Classifier

## -- L --

LaplacianKernelLeastSquaresClassifier | Laplacian Regularized Least Squares Classifier
LaplacianSVM | Laplacian SVM classifier
LearningCurveSSL | Compute Semi-Supervised Learning Curve
LearningCurveSSL.matrix | 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
line_coefficients | Loss of a classifier or regression function
line_coefficients-method | Loss of a classifier or regression function
localDescent | Local descent
LogisticLossClassifier | Logistic Loss Classifier
LogisticLossClassifier-class | LogisticLossClassifier
LogisticRegression | (Regularized) Logistic Regression implementation
LogisticRegressionFast | Logistic Regression implementation that uses R's glm
logsumexp | Numerically more stable way to calculate log sum exp
loss | Loss of a classifier or regression function
loss-method | Loss of a classifier or regression function
losslogsum | LogsumLoss of a classifier or regression function
losslogsum-method | LogsumLoss of a classifier or regression function
losspart | Loss of a classifier or regression function evaluated on partial labels
losspart-method | Loss of a classifier or regression function evaluated on partial labels

## -- M --

MajorityClassClassifier | Majority Class Classifier
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
measure_accuracy | Performance measures used in classifier evaluation
measure_error | Performance measures used in classifier evaluation
measure_losslab | Performance measures used in classifier evaluation
measure_losstest | Performance measures used in classifier evaluation
measure_losstrain | 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

## -- N --

NearestMeanClassifier | Nearest Mean Classifier

## -- P --

plot.CrossValidation | Plot CrossValidation object
plot.LearningCurve | Plot LearningCurve object
posterior | Class Posteriors of a classifier
posterior-method | Class Posteriors of a classifier
predict-method | Predict for matrix scaling inspired by stdize from the PLS package
predict-method | Predict using RSSL classifier
PreProcessing | Preprocess the input to a classification function
PreProcessingPredict | Preprocess the input for a new set of test objects for classifier
print.CrossValidation | Print CrossValidation object
print.LearningCurve | Print LearningCurve object
projection_simplex | project an n-dim vector y to the simplex Dn

## -- Q --

QuadraticDiscriminantClassifier | Quadratic Discriminant Classifier

## -- R --

responsibilities | Responsibilities assigned to the unlabeled objects
responsibilities-method | Predict using RSSL classifier
RSSL | R Semi-Supervised Learning Package
rssl-formatting | Show RSSL classifier
rssl-predict | Predict using RSSL classifier

## -- S --

S4VM | Safe Semi-supervised Support Vector Machine (S4VM)
S4VM-class | LinearSVM Class
sample_k_per_level | Sample k indices per levels from a factor
scaleMatrix | Matrix centering and scaling
SelfLearning | Self-Learning approach to Semi-supervised Learning
show-method | Show RSSL classifier
solve_svm | SVM solve.QP implementation
split_dataset_ssl | Create Train, Test and Unlabeled Set
split_random | Randomly split dataset in multiple parts
SSLDataFrameToMatrices | Convert data.frame to matrices for semi-supervised learners
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
SVM | SVM Classifier
svmlin | svmlin implementation by Sindhwani & Keerthi (2006)
svmlin_example | Test data from the svmlin implementation
svmproblem | Train SVM

## -- T --

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
TSVM | Transductive SVM classifier using the convex concave procedure

## -- U --

USMLeastSquaresClassifier | Updated Second Moment Least Squares Classifier
USMLeastSquaresClassifier-class | USMLeastSquaresClassifier

## -- W --

wdbc | wdbc data for unit testing
WellSVM | WellSVM for Semi-supervised Learning
wellsvm_direct | wellsvm implements the wellsvm algorithm as shown in [1].
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
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