Multiple-instance learning (MIL) is a significant weakly supervised learning problem, where
the training data consist of bags containing multiple instances and bag-level labels. Most …

Positive-unlabeled (PU) learning aims to train classifiers using positive and unlabeled
samples. Most methods assume a selected completely at random labeling scenario, which …

Variable selection is important for classification of data with many irrelevant predicting
variables, but it has not yet been well studied in positive-unlabeled (PU) learning, where …

In recent years, the feedback attitude towards teachers has become one of the most
important factors affecting learners' trust in teachers' teaching ability and changing learning …

Graph-valued data arises in numerous diverse scientific fields ranging from sociology,
epidemiology and genomics to neuroscience and economics. For example, sociologists …

Аннотация В статье представлен пример использования образовательного портала
как средство совершенствования образовательного процесса на основе …