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