{ "_id": "6a1eef31b401979e73412b18", "Package": "RSSL", "Version": "0.9.8", "Title": "Implementations of Semi-Supervised Learning Approaches for\nClassification", "Authors@R": "c(person(\"Jesse\", \"Krijthe\", role = c(\"aut\", \"cre\"),\nemail = \"jkrijthe@gmail.com\"))", "Description": "A collection of implementations of semi-supervised\nclassifiers and methods to evaluate their performance. The\npackage includes implementations of, among others, Implicitly\nConstrained Learning, Moment Constrained Learning, the\nTransductive SVM, Manifold regularization, Maximum Contrastive\nPessimistic Likelihood estimation, S4VM and WellSVM.", "License": "GPL (>= 2)", "URL": "https://github.com/jkrijthe/RSSL", "BugReports": "https://github.com/jkrijthe/RSSL/issues", "Roxygen": "list(old_usage = TRUE)", "Collate": "'Generics.R' 'Classifier.R' 'CrossValidation.R'\n'LeastSquaresClassifier.R' 'EMLeastSquaresClassifier.R'\n'NormalBasedClassifier.R' 'LinearDiscriminantClassifier.R'\n'EMLinearDiscriminantClassifier.R' 'NearestMeanClassifier.R'\n'EMNearestMeanClassifier.R' 'LogisticRegression.R'\n'EntropyRegularizedLogisticRegression.R' 'Evaluate.R'\n'GRFClassifier.R' 'GenerateSSLData.R' 'HelperFunctions.R'\n'ICLeastSquaresClassifier.R' 'ICLinearDiscriminantClassifier.R'\n'KernelLeastSquaresClassifier.R'\n'KernelICLeastSquaresClassifier.R'\n'LaplacianKernelLeastSquaresClassifier.R' 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"page": "print.CrossValidation", "title": "Print CrossValidation object", "topics": [ "print.CrossValidation" ] }, { "page": "print.LearningCurve", "title": "Print LearningCurve object", "topics": [ "print.LearningCurve" ] }, { "page": "projection_simplex", "title": "Project an n-dim vector y to the simplex Dn", "topics": [ "projection_simplex" ] }, { "page": "QuadraticDiscriminantClassifier", "title": "Quadratic Discriminant Classifier", "concept": [ "RSSL classifiers" ], "topics": [ "QuadraticDiscriminantClassifier" ] }, { "page": "responsibilities-methods", "title": "Responsibilities assigned to the unlabeled objects", "topics": [ "responsibilities" ] }, { "page": "rssl-formatting", "title": "Show RSSL classifier", "topics": [ "rssl-formatting", "show,Classifier-method", "show,NormalBasedClassifier-method", "show,scaleMatrix-method" ] }, { "page": "rssl-predict", "title": "Predict using RSSL classifier", "topics": [ "decisionvalues,WellSVM-method", "predict,GRFClassifier-method", 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Semi-supervised Learning", "concept": [ "RSSL classifiers" ], "topics": [ "SelfLearning" ] }, { "page": "solve_svm", "title": "SVM solve.QP implementation", "topics": [ "solve_svm" ] }, { "page": "split_dataset_ssl", "title": "Create Train, Test and Unlabeled Set", "concept": [ "RSSL utilities" ], "topics": [ "split_dataset_ssl" ] }, { "page": "split_random", "title": "Randomly split dataset in multiple parts", "concept": [ "RSSL utilities" ], "topics": [ "split_random" ] }, { "page": "SSLDataFrameToMatrices", "title": "Convert data.frame to matrices for semi-supervised learners", "concept": [ "RSSL utilities" ], "topics": [ "SSLDataFrameToMatrices" ] }, { "page": "stat_classifier", "title": "Plot RSSL classifier boundaries", "topics": [ "stat_classifier" ] }, { "page": "stderror", "title": "Calculate the standard error of the mean from a vector of numbers", "topics": [ "stderror" ] }, { "page": "summary.CrossValidation", "title": "Summary of Crossvalidation results", "topics": [ 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