Semi-supervised learning

Time series feature learning with labeled and unlabeled data

Time series classification has attracted much attention in the last two decades. However, in many real-world applications, the acquisition of sufficient amounts of labeled training data is costly, while unlabeled data is usually easily to be …

Classifying networked text data with positive and unlabeled examples

The rapid growth in the number of networked applications that naturally generate complex text data, which contains not only inner features but also inter-dependent relations, has created the demand of efficiently classifying such data. Many …